A demand response based charging and discharging power dynamic adjustment method

Abstract

The application discloses a kind of based on demand response's charge-discharge power dynamic adjustment method, it is related to charge-discharge power control technical field, the method includes: historical data calling is carried out to target power pool, obtains historical discharge data;Obtain discharge demand analysis model by construction;preset environment acquisition condition carries out data collection to power supply area, obtains stable environment data;Power supply mode identification is carried out, and real-time power supply mode is obtained;Stable environment data and real-time power supply mode are synchronized to the discharge demand analysis model, and real-time discharge demand is obtained;Real-time discharge capacity is calculated and obtained, and charging power is adjusted.The application solves the technical problems that exist in the prior art, such as low energy utilization efficiency of power pool, poor grid load optimization effect, high operating cost, and insufficient reliability and stability of power grid, to improve the energy utilization efficiency of power pool, optimize the grid load, reduce the operating cost, and enhance the reliability and stability of the technical effect of power grid.

Description

Technical Field

[0001] This invention relates to the field of charging and discharging power control technology, and more specifically to a method for dynamically adjusting charging and discharging power based on demand response. Background Technology

[0002] In today's society, the continuous growth in energy demand places higher demands on the development of sustainable energy. Electricity, as a crucial energy source in modern society, requires a stable and efficient supply. However, traditional power systems have limitations in adjusting charging and discharging power. Traditional methods often lack sufficient consideration of demand response, making it difficult to accurately predict changes in electricity demand, resulting in low energy efficiency and uneven grid load distribution. This not only increases operating costs but may also cause grid instability, affecting the reliability of power supply. Furthermore, changes in environmental factors have a significant impact on electricity demand. Electricity demand varies across different seasons and time periods, and traditional methods often struggle to accurately capture these changes, failing to flexibly adjust charging and discharging power according to actual needs.

[0003] Existing technologies suffer from low energy utilization efficiency of power pools, poor grid load optimization, high operating costs, and insufficient grid reliability and stability. Summary of the Invention

[0004] This application provides a demand-response-based dynamic adjustment method for charging and discharging power, which addresses the technical problems in the prior art, such as low energy utilization efficiency of power pools, poor grid load optimization, high operating costs, and insufficient grid reliability and stability.

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

[0006] Historical data is retrieved from the target power pool to obtain historical discharge data, wherein the target power pool integrates multiple energy storage units; discharge demand analysis is performed based on the historical discharge data to construct a discharge demand analysis model; data is collected from the power supply area under preset environmental acquisition conditions to obtain stable environmental data, wherein the stable environmental data has a stable window identifier; power supply mode identification is performed based on the stable window identifier to obtain the 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 the real-time discharge demand; the real-time discharge amount is calculated according to the stable window identifier and the real-time discharge demand, and it is determined 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, tasks are allocated to the multiple energy storage units, and the charging power of the target power pool is adjusted based on the task allocation results.

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

[0008] Historical data of the target power pool is retrieved to obtain historical discharge data. Based on this historical discharge data, discharge demand analysis is performed to construct a discharge demand analysis model. Data is collected from the power supply area under preset environmental acquisition conditions to obtain stable environmental data. Power supply mode identification is performed to obtain the 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 the real-time discharge demand. The real-time discharge amount is calculated, and it is determined 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, tasks are allocated to the multiple energy storage units, and the charging power of the target power pool is adjusted based on the task allocation results. This achieves 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 power grid. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating a demand-response-based dynamic adjustment method for charging and discharging power provided in an embodiment of this application;

[0011] Figure 2 This is a flowchart illustrating the process of obtaining a real-time power supply mode in a demand-response-based dynamic adjustment method for charging and discharging power, as provided in an embodiment of this application. Detailed Implementation

[0012] This application provides a demand-response-based dynamic adjustment method for charging and discharging power, which addresses the technical problems in existing technologies such as low energy utilization efficiency of power pools, poor grid load optimization, high operating costs, and insufficient grid reliability and stability.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] Examples, such as Figure 1 As shown, this application provides a method for dynamic adjustment of charging and discharging power based on demand response, the method comprising:

[0015] Step S100: Retrieve historical data from the target power pool to obtain historical discharge data, wherein the target power pool integrates multiple energy storage units.

[0016] Specifically, historical data from the target power pool, a crucial facility integrating multiple energy storage units, is retrieved. This historical data, containing rich information such as discharge magnitude, frequency, and duration over different time periods, is essential for analyzing the power pool's performance and behavior patterns. Studying this data allows us to understand the power pool's discharge characteristics under varying environmental conditions and usage scenarios, providing a strong basis for subsequent analysis and decision-making. For example, historical data can be used to identify peak and off-peak electricity consumption periods within specific timeframes, enabling better planning of the power pool's charging and discharging strategies and improving energy efficiency and system stability.

[0017] Step S200: Based on the historical discharge data, perform discharge demand analysis and construct a discharge demand analysis model.

[0018] Specifically, based on the acquired historical discharge data, an in-depth discharge demand analysis is conducted. First, the historical discharge data is segmented according to seasons to obtain multiple seasonal discharge data, which better considers the impact of different seasons on discharge demand. Next, a seasonal transition period is preset, and discharge data from adjacent seasons are sampled. Based on the sampling results, the discharge data from multiple seasons are mapped and overlapped to obtain multiple reliable discharge data, which more accurately reflect the actual discharge situation. Then, the first reliable discharge data is segmented based on discharge time to obtain daytime reliable discharge data and nighttime reliable discharge data. Next, the daytime reliable discharge data is decomposed to obtain multiple sample discharge power and multiple sample environmental data. Random operators are called from the operator library, and these sample discharge power and sample environmental data are used to train and optimize the random operators, thereby obtaining a daytime power analysis sub-branch. Similarly, a 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, which is identified by the first season. By analogy, multiple power prediction branches are constructed, and these branches are connected in parallel to ultimately complete the discharge demand analysis model. This model can accurately analyze and predict discharge demand based on different seasons and times, while improving energy utilization efficiency and system stability.

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

[0020] Specifically, environmental acquisition conditions are pre-set to collect data from the power supply area. These conditions include specific sensor types and layouts, acquisition time intervals, and data accuracy requirements. These pre-set conditions ensure the accuracy and reliability of the collected data. During data collection from the power supply area, stable environmental data is obtained, reflecting various environmental factors such as temperature, humidity, and light intensity. These environmental factors affect the performance and discharge requirements of the target power battery. Stable environmental data has a stability window indicator, which indicates that the data is relatively stable within a certain time range. For example, if the environmental data shows minimal variation within a specific time period, it can be considered stable and assigned a stability window indicator. This indicator aids in subsequent data analysis and processing, as stable data provides a more reliable reference, helping to better understand the environmental conditions of the power supply area and its impact on the target power battery.

[0021] Step S400: Based on the stable window identifier, identify the power supply mode to obtain the real-time power supply mode.

[0022] Specifically, power supply mode identification is based on stable window identifiers to obtain the real-time power supply mode. The stable environmental data represented by the stable window identifiers provides an important reference for power supply mode identification. Analyzing the environmental data corresponding to the stable window identifiers, including factors such as temperature, humidity, and light intensity, as well as other factors affecting power supply, reflects the basic state of the current power supply area. Then, combining historical power supply data with the current environmental conditions, the power supply mode is determined. Power supply modes include various types, such as peak-hour power supply, off-peak-hour power supply, stable power supply, and emergency power supply. Through comprehensive analysis of environmental data and historical patterns, the current real-time power supply mode is determined. For example, if the stable window identifiers show that the current ambient temperature is moderate, the power load is relatively stable, and historical data typically shows a stable power supply mode under similar conditions, then the current real-time power supply mode can be determined to be stable power supply. In certain special circumstances, such as sudden weather changes or a surge in power demand, an emergency power supply mode or other special power supply modes will be identified. Obtaining the real-time power supply mode is crucial for rationally adjusting the charging and discharging strategy of the target power battery. Different power supply modes imply different power supply stability and reliability, thus affecting the discharge demand and charging opportunities of the target power battery. By accurately identifying real-time power supply modes, the operation of the target power pool can be better planned, thereby improving the reliability and stability of the power grid.

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

[0024] Specifically, stable environmental data and real-time power supply patterns are synchronously transmitted to the discharge demand analysis model to obtain real-time discharge demand. Stable environmental data includes information on various environmental factors in the power supply area, such as temperature, humidity, and light intensity. This data reflects the current state of the power supply environment and affects the discharge demand of the target power battery. For example, higher temperatures can lead to a decrease in the power battery's discharge efficiency, thus increasing discharge demand to meet the same electrical load. The real-time power supply pattern indicates the current power supply situation, including power supply stability, voltage level, and whether it is during peak or off-peak hours. Different power supply patterns directly affect the target power battery's operating strategy and discharge demand. For example, during peak hours when power supply is tight, it is necessary to more precisely control the power battery's discharge to ensure power supply to critical loads. These stable environmental data and real-time power supply patterns are synchronously input into the discharge demand analysis model. This model comprehensively considers these factors and uses pre-established algorithms and model structures to analyze and process the input data. The model adjusts its internal parameters and calculation methods according to different environmental factors and power supply patterns to accurately predict the real-time discharge power demand under the current conditions. This method allows for more accurate real-time discharge power demand, providing a scientific basis for subsequent charging and discharging management of the target power battery. For example, based on the real-time discharge power demand, it can be determined whether the power battery needs to be charged to meet upcoming peak electricity demand, or whether the discharge power can be adjusted to adapt to different power supply modes and environmental conditions. This improves the energy utilization efficiency of the power battery, optimizes grid load, reduces operating costs, and enhances the reliability and stability of the power grid.

[0025] Step S600: Calculate the real-time discharge amount based on the stable window identifier and the real-time discharge requirement, and determine whether the remaining power of the target power pool meets the real-time discharge amount.

[0026] Specifically, the real-time discharge quantity is first calculated based on the stable environmental data represented by the stability window indicator and the real-time discharge demand determined in the previous steps. The stability window indicator provides information on the relatively stable environmental conditions, which affect factors such as discharge efficiency and rate, while the real-time discharge demand clarifies the required power output under current conditions. By combining these two factors, and comprehensively considering the impact of environmental factors on discharge and actual power demand, the real-time discharge quantity is accurately calculated. Next, it is determined whether the remaining power of the target power pool meets this real-time discharge quantity. The current remaining power of the target power pool is compared with the calculated real-time discharge quantity. If the remaining power is greater than or equal to the real-time discharge quantity, it indicates that the target power pool can meet the power demand under current conditions, and no emergency adjustment measures are required. However, if the remaining power is less than the real-time discharge quantity, it means that the target power pool cannot continuously meet the current power demand, and corresponding measures need to be taken to adjust the operating status of the power pool. For example, tasks may be allocated among multiple energy storage units to adjust charging power, or other measures may be taken to ensure the stability and reliability of power supply. This judgment process is crucial for timely detection of potential power supply problems and the implementation of effective countermeasures.

[0027] Step S700: If the remaining power of the target power pool does not meet the real-time discharge amount, then 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.

[0028] Specifically, when the remaining power in the target power pool is insufficient to meet the current real-time discharge demand, 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. This deficiency occurs during external discharge, when some energy storage units are switching from charging to discharging. In this situation, a rapid response and corresponding adjustments are required. Task allocation for multiple energy storage units within the target power pool is determined based on the current state of each unit. Each unit is in a different state; some are charging, while others have just completed charging and are about to enter a discharging state. The purpose of task allocation is to find the optimal match between power demand and the state of the energy storage units. For example, if some energy storage units have sufficient power and have just completed their charging task, they will be assigned to discharging tasks to quickly replenish the power supply.

[0029] After task allocation is completed, the charging power of the target power pool is adjusted based on the allocation results. For example, if certain energy storage units need to be charged first to prepare for future discharge demands, the charging rate or charging time of these units is adjusted to ensure they reach the required capacity before the next demand arrives. Simultaneously, if some energy storage units are charging and need to switch to a discharge task, their charging power is reduced or their charging is temporarily interrupted to meet real-time discharge demands more quickly. By dynamically adjusting task allocation and charging power, changes in electricity demand can be flexibly addressed, existing energy storage resources can be utilized to the maximum extent, and the stable operation of the power grid can be ensured.

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

[0031] Step S210: Segment the historical discharge data based on the season to obtain multiple seasonal discharge data.

[0032] Step S220: Sample discharge data of adjacent seasons during the preset seasonal transition period, and perform mapping and overlapping processing of the multiple seasonal discharge data based on the sampling results to obtain multiple reliable discharge data.

[0033] Step S230: Based on the discharge time segmentation of the first reliable discharge data, obtain daytime reliable discharge data and nighttime reliable discharge data, wherein the first reliable discharge data is any one of the plurality of reliable discharge data.

[0034] Step S240: Disassemble the daytime reliable discharge data to obtain multiple sample discharge power and multiple sample environmental data.

[0035] Step S250: Call random operators from the operator library, and use the multiple sample discharge power and multiple sample environmental data to train and optimize the obtained random operators to obtain the daytime power analysis sub-branch.

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

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

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

[0039] Specifically, to analyze historical discharge data more effectively, it is segmented according to the season. Since climate conditions and electricity demand vary significantly across seasons, this segmentation helps to gain a deeper understanding of the discharge characteristics of the target power battery in each season. Through detailed analysis of historical discharge data, the data is divided into four seasons: spring, summer, autumn, and winter, resulting in discharge data for multiple seasons. For example, in summer, due to higher temperatures and frequent use of high-power appliances such as air conditioners, the discharge volume is larger and the discharge pattern differs from other seasons; while in winter, the use of heating equipment also exhibits specific discharge patterns. This segmentation provides a foundation for building a more accurate discharge demand analysis model, enabling the model to better adapt to seasonal changes and improve the accuracy of discharge demand prediction.

[0040] Considering the unique changes in data brought about by seasonal transitions, a seasonal transition period is pre-defined. During this transition period, discharge data from adjacent seasons are sampled, selecting representative data from the large amounts of discharge data in each of the two adjacent seasons. For example, data points can be randomly selected according to a certain proportion, or key data can be selected based on specific time nodes. Then, based on these sampling results, a mapping and overlay process is applied to the discharge data from multiple seasons. Because there is often some overlap in electricity consumption and environmental factors between the end of one season and the beginning of the next during seasonal transitions—for example, at the end of spring and the beginning of summer, the temperature is in a transitional state, and electricity demand exhibits both spring characteristics and summer trends—this mapping and overlay process merges the data from adjacent seasons during the transition period, allowing for a smoother transition of discharge data between seasons. After this process, multiple reliable discharge data points are obtained. These data, after considering the impact of seasonal transitions, have higher reliability and accuracy. They provide a more solid data foundation for subsequent analysis and model building, ensuring that the discharge demand analysis model can better adapt to seasonal changes and accurately predict discharge demand in different seasons and during seasonal transitions.

[0041] We select any one data point from multiple reliable discharge datasets, referred to here as the first reliable discharge data. Due to significant differences in electricity consumption behavior and environmental factors between daytime and nighttime, to analyze the discharge situation more deeply, we segment the first reliable discharge data based on discharge time. According to a certain time standard, the first reliable discharge data is divided into two parts: daytime reliable discharge data and nighttime reliable discharge data. For example, the discharge data can be divided into daytime and nighttime parts using the local sunrise and sunset times as the boundary. During the day, people's production and daily life activities are usually more frequent, resulting in higher electricity demand, and environmental factors such as sunlight also affect discharge; while at night, many production activities cease, electricity demand changes, and the environment is relatively stable. This segmentation method allows for more detailed analysis of the different characteristics of daytime and nighttime, providing more specific data support for subsequently building a more accurate discharge demand analysis model.

[0042] The reliable daytime discharge data is decomposed. Since this data contains a wealth of information, it can be broken down into more specific data elements, extracting multiple sample discharge power and environmental data. Sample discharge power reflects the discharge power of the target power cell at different times or under different usage scenarios during the day. These different discharge power values ​​help analyze changes in electricity demand during the daytime and the factors that may affect discharge power. Sample environmental data includes various discharge-related environmental factors during the day, such as temperature, humidity, and light intensity. This environmental data is crucial for understanding daytime discharge patterns, as environmental factors often influence the performance and discharge behavior of the target power cell. Obtaining these multiple sample discharge power and environmental data by decomposing the reliable daytime discharge data provides more detailed and specific foundational data for subsequent analysis and model building, helping to more accurately grasp the patterns and characteristics of daytime discharge, thus laying the foundation for building a more precise discharge demand analysis model.

[0043] First, random operators are called from the operator library. This library stores various types of operators used for data processing and analysis. The selection of random operators is based on specific algorithmic requirements or to introduce randomness and diversity to improve the accuracy and robustness of subsequent analysis. Next, the called random operators are trained and optimized using multiple sample discharge power and environmental data. These specific sample data are input into the random operators, and by continuously adjusting the operator's parameters and structure, they are made to better adapt to the characteristics of daytime discharge data. During training, the random operators learn and adjust based on the input data to minimize prediction errors or maximize certain performance indicators. After training and optimization, a 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 trends based on new input daytime discharge 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, contributing to the construction of a more accurate and efficient discharge demand analysis model.

[0044] A similar approach to constructing the daytime power analysis sub-branch was adopted to build the nighttime power analysis sub-branch. Since nighttime electricity consumption behavior and environmental factors differ significantly from daytime, a separate analysis is necessary. First, referencing the process of constructing the daytime power analysis sub-branch, a suitable random operator for processing nighttime data was called from the operator library. This random operator differs from the one used for daytime data in parameter settings and structure to better adapt to the characteristics of nighttime discharge data. Then, the called random operator was trained and optimized using multiple sample discharge power data and multiple sample environmental data from reliable nighttime discharge data. During training, the operator parameters were continuously adjusted based on nighttime electricity consumption patterns and environmental characteristics to accurately analyze nighttime discharge conditions. After training and optimization, the nighttime power analysis sub-branch was successfully constructed. This sub-branch is specifically designed to analyze nighttime discharge power demand and trends. It can provide strong support for nighttime power management and planning based on input nighttime discharge and environmental data. By constructing the nighttime power analysis sub-branch in conjunction with the daytime power analysis sub-branch, a more comprehensive analysis of the target power pool's discharge demand at different time periods can be achieved, laying the foundation for building a complete discharge demand analysis model.

[0045] By parallelizing the daytime power analysis branch and the nighttime power analysis branch, two analysis branches specifically designed for different time periods (daytime and nighttime) are connected together, enabling them to work together. Through parallel operation, these two branches can simultaneously receive input data, analyze it separately based on the characteristics of daytime and nighttime, and then integrate the results. This parallel operation yields the first power prediction branch, which integrates the power analysis capabilities of both daytime and nighttime. It predicts power demand for different time periods based on various input data, including time information and environmental data. For example, it determines whether it is daytime or nighttime based on the current time, then calls the corresponding sub-branch for analysis, ultimately deriving a comprehensive power prediction result. Simultaneously, the first power prediction branch is labeled with a first-season identifier. This is because electricity demand and environmental factors vary across seasons, and this branch is built for a specific season. The seasonal identifier clearly defines the applicable seasonal range for the branch, facilitating differentiation and retrieval in subsequent analysis and applications. Such labeling helps to better manage and utilize power prediction branches for different seasons, improving the accuracy and applicability of the entire discharge demand analysis model.

[0046] Following a similar approach to constructing the first power prediction branch, multiple power prediction branches can be constructed by repeating the previous steps for different seasons or other specific conditions. Each power prediction branch is customized for different seasons, time periods, or electricity usage scenarios. For example, one power prediction branch can be constructed for daytime and nighttime conditions in spring, and another branch can be constructed for different conditions in summer, and so on. After constructing multiple power prediction branches, these branches are connected in parallel to complete the construction of the discharge demand analysis model. The parallel process allows each branch to perform analysis independently while integrating the results to provide a more comprehensive and accurate power prediction for the entire system. This discharge demand analysis model selects an appropriate power prediction branch for analysis based on different input conditions, or combines the results of multiple branches to derive the final discharge demand prediction. 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 discharge demand analysis of the target power battery, and provide strong decision support for reasonable charge and discharge management.

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

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

[0049] Specifically, environmental data acquisition conditions are clearly defined, including acquisition trigger conditions and acquisition suppression conditions. Acquisition trigger conditions determine when environmental data acquisition begins. For example, environmental data acquisition is triggered when a specific time point is reached, the status of a device changes, or other preset conditions are met. Acquisition suppression conditions, on the other hand, come into play when environmental data changes; once the suppression condition is met, environmental data acquisition stops. This is to ensure the stability and reliability of the acquired data, avoiding unnecessary acquisition when environmental data fluctuates frequently. It is worth noting that the acquisition trigger condition is actually the suppression condition of the previous environmental acquisition. This design gives the environmental data acquisition process a certain degree of continuity and logic. When acquisition is suppressed in one instance, the trigger condition for the next acquisition will be set based on the previous suppression, thus ensuring the orderly progress and validity of data acquisition. By reasonably setting acquisition trigger and suppression conditions, the timing and frequency of environmental data acquisition can be better controlled, improving the efficiency and quality of data acquisition.

[0050] In one possible implementation, such as Figure 2 As shown, step S400 further 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, then generate the first transition season and the 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 first step is to determine whether the stable window indicator falls within a seasonal transition period. If the stable window indicator falls within a seasonal transition period, it means that the current environmental data is at the boundary between two seasons. In this case, a first transition season and a second transition season will be generated, clearly identifying which two seasons are in transition. For example, if we are currently in the transition period from spring to summer, then the first transition season might be spring, and the second transition season might be summer. This division helps to more accurately grasp the special electricity consumption and environmental factors during seasonal transitions. However, if the stable window indicator does not fall within a seasonal transition period, it indicates that we are currently in a relatively stable season. In this case, the current season can be directly determined without generating a transition season indicator.

[0055] Discharge time is determined based on a stable window identifier. By analyzing the current time information, it is determined whether it is during different time periods, such as daytime or nighttime, thus obtaining the real-time power supply type. Different power supply types may correspond to different power demands and power supply characteristics.

[0056] By integrating and storing real-time power supply type, first transition season, and second transition season, a real-time power supply pattern is obtained. First, the real-time power supply type reflects the specific characteristics of the current power supply, such as peak supply, off-peak supply, or stable supply. The first and second transition seasons clarify whether the current situation is a special period of seasonal transition and the two specific seasons involved. By integrating and storing this information, a comprehensive description of the current power supply situation can be obtained. For example, if the real-time power supply type is peak supply and it is in the transition season from spring to summer, then this integrated information forms a specific real-time power supply pattern. This pattern not only considers the temporal characteristics of power supply but also incorporates the impact of seasonal changes. Such a real-time power supply pattern is crucial for subsequent analysis and decision-making. It can provide accurate data for the management of the target power pool, helping to determine how to perform charging and discharging operations under the current power supply pattern to better meet electricity demand, while improving energy utilization efficiency and system stability. By comprehensively considering different power supply conditions and seasonal changes, it is possible to more flexibly respond to various complex power supply environments, ensuring the reliability and sustainability of power supply.

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

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

[0059] Step S520: Activate the first power analysis sub-branch and the 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 for power prediction, and obtain the first real-time prediction result and the second real-time prediction result.

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

[0062] Specifically, seasonal identifiers are used for mapping operations to activate specific branches in the discharge demand analysis model. When it is determined that the current period is a seasonal transition, identifiers for the first and second transition seasons are generated. Based on these seasonal identifiers, the model can accurately identify the corresponding first and second real-time prediction branches. By activating these specific real-time prediction branches, the model can more accurately analyze situations during seasonal transitions, taking into account changes in electricity demand and the impact of environmental factors in different transition seasons, providing a more targeted basis for subsequent power forecasting and discharge demand analysis. This improves the model's adaptability and accuracy during seasonal transitions, better meeting the power management needs of different seasonal transition phases.

[0063] Based on the real-time power supply type, further mapping operations are performed in the first and second real-time prediction branches to activate the corresponding first and second power analysis sub-branches. The real-time power supply type reflects the specific situation of the current power supply, such as peak supply, off-peak supply, or stable supply. Different power supply types imply different electricity demand and power supply characteristics. Once the real-time power supply type is determined, the corresponding power analysis sub-branches are found in the first and second real-time prediction branches. These sub-branches are specifically designed for different combinations of power supply types and transitional seasons. For example, the first power analysis sub-branch is suitable for the first transitional season and performs power analysis under a specific power supply type, taking into account the environmental factors of that transitional season and the impact of the specific power supply type. Similarly, the second power analysis sub-branch performs power analysis for the second transitional season and the corresponding power supply type.

[0064] Stable environmental data is synchronously transmitted to both the first and second power analysis sub-branches for power prediction. This stable environmental data includes information on various environmental factors related to power system operation, such as temperature, humidity, and light intensity. This stable environmental data is simultaneously input into both the first and second power analysis sub-branches, which are designed for different transitional seasons but both utilize the input environmental data to analyze and predict power. The first power analysis sub-branch predicts power for the corresponding transitional season based on the received stable environmental data and its specific internal algorithms and model structure, thus obtaining a 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 transitional season. Similarly, the second power analysis sub-branch analyzes the input stable environmental data to derive a second real-time prediction result, representing the predicted power value under the second transitional season and current environmental conditions.

[0065] The first and second real-time prediction results are averaged. By averaging, the prediction results of the two transitional seasons and their corresponding power analysis sub-branches can be comprehensively considered, resulting in a more accurate real-time discharge demand. This calculation method can balance the impact of different seasons and power supply types during seasonal transitions, providing a reliable basis for the charging and discharging management of the target power battery.

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

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

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

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

[0070] Step S640: At the start time identified by the update window, perform charging management of the plurality of energy storage units according to the charging scheduling sequence.

[0071] Specifically, the first step is to determine whether the remaining capacity of the target power pool meets the real-time discharge requirement. If so, it means the target power pool has sufficient capacity to meet current electricity demand. At this point, electricity price requests are made based on a stability window identifier, which represents a specific time period, specific market conditions, or other electricity price-related constraints. This identifier determines the scope within which electricity price queries and requests can be made. For example, the stability window identifier may limit the query to electricity price information within a specific region and time period. After the electricity price request is made, a real-time electricity price sequence is obtained. This sequence contains electricity price data at different points in time or under different conditions within the range determined by the stability window identifier, under the given conditions. This electricity price data helps decision-makers understand current electricity price trends and changes.

[0072] First, the charging cycle time required to fully charge the target power battery from zero capacity is determined interactively. This charging cycle time is crucial for planning the power battery's usage, as it reflects the charging speed and capacity of the target power battery under different conditions. Considering that charging and discharging need to be synchronized to ensure the sustainability of the power battery and prevent it from becoming a disposable device, one-third of the charging cycle time is used to update the stability window identifier. The stability window identifier originally represents a specific time range or condition; by updating it with a portion of the charging cycle time, the window more closely matches the actual charging and discharging rhythm of the power battery. The resulting updated window identifier can better reflect the operating status of the power battery and the trend of electricity price changes over a future period. Electricity price is invoked based on the updated window identifier. Within the new window range, the corresponding electricity price information is queried and obtained, resulting in an updated electricity price sequence. This sequence contains electricity price data at different times or under different conditions within the updated time window. By continuously updating the window and obtaining new electricity price sequences, the real-time changes in electricity prices are monitored, providing a more accurate basis for rationally scheduling the charging and discharging operations of the power battery, thereby achieving more efficient energy management and cost control.

[0073] The updated electricity price sequence is compared 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 take full advantage, the remaining energy storage information of multiple energy storage units is obtained interactively. This remaining energy storage information reflects how much electricity each energy storage unit can still store. Then, a charging scheduling sequence is generated based on these multiple remaining energy storage information. This charging scheduling sequence is a charging plan formulated for multiple energy storage units, which takes into account factors such as the remaining energy storage capacity of each energy storage unit, charging priority, and overall charging efficiency. For example, energy storage units with less remaining energy storage but higher charging priority will be charged first, or energy storage units with faster charging speeds can be assigned more charging tasks.

[0074] When the start time determined by the updated window identifier is reached, charging management is performed on multiple energy storage units according to the previously generated charging scheduling sequence. This start time is determined based on the charging cycle time of the target power pool and the update of the stability window identifier, representing a new time node with more favorable electricity price conditions or other suitable conditions for charging. The charging scheduling sequence is a carefully crafted charging plan based on the remaining energy storage status of multiple energy storage units. It specifies the charging order, charging amount, and charging speed of each energy storage unit. At the start time of the updated window identifier, charging operations are managed sequentially for multiple energy storage units according to this charging scheduling sequence. For example, charging begins first on energy storage units with less remaining energy and higher priority. Parameters such as charging current and voltage are adjusted according to their charging needs and charging capacity to ensure a safe and efficient charging process. Then, following the sequence, the charging management of other energy storage units is carried out step by step. Throughout the process, the charging status of each energy storage unit is monitored in real time, such as changes in power and temperature, so as to adjust the charging strategy in a timely manner and prevent problems such as overcharging and overheating. In this way, multiple energy storage units can be charged and managed in the most reasonable way at the optimal time, improving energy utilization efficiency, reducing charging costs, and ensuring the stable operation and reliability of the entire energy storage system.

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

[0076] Step S710: Calculate the power supply duration of multiple units based on the real-time discharge demand and multiple remaining energy storage.

[0077] Step S720: Interact to obtain multiple unit states of the multiple energy storage units, wherein each unit state includes quantified health, historical charging frequency, historical discharging frequency and remaining service life.

[0078] Step S730: Pre-construct a demand response quantization function, and obtain multiple demand response indices by synchronizing the power supply duration and status of the multiple units to the demand response quantization function.

[0079] Step S740: Forward serialize the plurality of demand response indices to sort the charging tasks of the plurality of energy storage units.

[0080] Step S750: Interact to obtain multiple charging power constraints of the multiple energy storage units.

[0081] Step S760: According to the charging task sorting, the multiple energy storage units are scheduled to dynamically adjust the charging power of the energy storage units according to the multiple charging power constraints.

[0082] Specifically, calculations are performed by combining real-time discharge demand with the remaining energy storage of each of the multiple energy storage units. Real-time discharge demand represents the current system's power requirement, reflecting the amount of electricity that needs to be extracted from the energy storage units within a specific time period. The remaining energy storage represents the current energy level stored in each energy storage unit. By comparing and calculating the real-time discharge demand with the remaining energy storage of each unit, the duration for which each energy storage unit can continuously supply power while meeting the current real-time discharge demand can be determined. For example, if an energy storage unit has a fixed remaining energy storage value and the real-time discharge demand is a fixed power value, then the power supply duration of that energy storage unit can be obtained through a simple division operation (remaining energy storage divided by the real-time discharge demand power).

[0083] Multiple energy storage units' statuses are obtained interactively. Each unit has specific status information, crucial for evaluating performance, reliability, and planning subsequent use and maintenance. Quantitative health reflects the unit's overall health; a higher health indicates good performance and reliable power storage and release, while a lower health suggests potential problems requiring further inspection or maintenance. Historical charging frequency records the number of times the unit has been charged. Frequent charging impacts lifespan and performance, such as reducing battery capacity and increasing internal resistance. Understanding historical charging frequency assesses usage intensity and aging. Historical discharging frequency records the number of times the unit has released power. Similar to charging frequency, high discharging frequency also affects the unit. Combining charging and discharging frequencies analyzes charge-discharge cycles, further understanding performance trends. Remaining lifespan indicates the expected time the unit can continue operating normally in its current state. Understanding remaining lifespan allows for strategic replacement planning, ensuring continuous and stable system operation.

[0084] A pre-constructed demand response quantification function is used to quantify the demand response capability of each energy storage unit by comprehensively considering multiple factors. The power supply duration and status of multiple units are synchronously input into the demand response quantification function. The power supply duration reflects the duration of the energy storage unit's ability to meet demand; a longer power supply duration generally means a more stable ability to meet power demand within a certain period. The multiple unit statuses include information such as quantified health, historical charging frequency, historical discharging frequency, and remaining lifespan. These status factors collectively affect the performance and reliability of the energy storage unit, thus influencing its demand response capability. The demand response quantification function processes and calculates these input parameters to ultimately obtain multiple demand response indices.

[0085] Multiple demand response indices are forward-sequentially processed, with higher values ​​indicating greater urgency. This means that energy storage units with higher demand response indices require charging more urgently. The demand response index comprehensively considers multiple factors, such as unit power supply duration, quantitative health status, historical charge / discharge frequency, and remaining lifespan. These factors collectively reflect the energy storage unit's ability to meet power demand and its current status. When an energy storage unit has a high demand response index, it indicates relatively poor performance in these aspects, possibly with low remaining power, low health status, or high usage frequency, thus urgently requiring charging to restore its optimal operating condition. By forward-sequencing these demand response indices, multiple energy storage units are sorted from highest to lowest urgency. This sorting result becomes the priority order for charging tasks. During charging scheduling, the system prioritizes charging energy storage units with high urgency, ensuring that those units crucial to system operation and urgently needing charging receive power replenishment as quickly as possible.

[0086] By interactively acquiring multiple charging power constraints for various energy storage units, each unit has specific charging power constraints due to its own characteristics, design specifications, and safety requirements. These constraints encompass several aspects. For example, the maximum charging power constraint limits the maximum power input the energy storage unit can withstand during charging; exceeding this power can damage the unit or cause safety issues. The minimum charging power constraint ensures that, under specific conditions, the energy storage unit can charge at a minimum power level to guarantee charging efficiency and stability. Furthermore, there are dynamic charging power constraints, such as power limits that change based on factors like unit temperature, remaining charge, and health status. Interacting with these constraints provides a comprehensive understanding of the limitations faced by each energy storage unit during charging. This is crucial for rationally scheduling charging and ensuring the safety and efficiency of the charging process. When scheduling charging and dynamically adjusting charging power, these constraints must be fully considered to avoid damage to the energy storage units due to violations of charging power constraints, while ensuring the stable and reliable operation of the entire energy storage system.

[0087] Multiple energy storage units are scheduled based on charging task priority, and their charging power is dynamically adjusted according to multiple charging power constraints. The charging task priority is determined by the demand response index, which clarifies the charging priority of each energy storage unit. High-priority energy storage units are typically those with weaker demand response capabilities and urgently need charging to improve overall system performance. During charging scheduling, charging is arranged for each energy storage unit in sequence according to the charging task priority. Simultaneously, considering that each energy storage unit has specific charging power constraints, including maximum and minimum charging power limits, the status and charging progress of each energy storage unit need to be monitored in real time during charging, and dynamic adjustments are made based on these charging power constraints. By continuously and dynamically adjusting the charging power, the charging needs of each energy storage unit can be met while ensuring that the charging process complies with safety regulations, improving the efficiency and reliability of the entire energy storage system, and achieving optimized charging management of multiple energy storage units.

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

[0089]

[0090] Where U is the demand response index, H is the quantitative health level, and F is the quantitative health level. c Based on historical charging frequency, F d L represents the historical discharge frequency, L represents the remaining service life, and T represents the unit power supply duration.

[0091] Specifically, U, the demand response index, is a quantitative indicator used to measure the energy storage unit's ability to respond to demand. H represents the quantitative health level, ranging from 0 to 1, where 1 indicates perfect health. The quantitative health level reflects the current performance status of the energy storage unit; a higher health level likely indicates a stronger ability to respond to demand. F c Historical charging frequency refers to the number of times the energy storage unit has been charged in the past. Frequent charging can affect the lifespan and performance of the unit. d Historical discharge frequency records the number of times the unit has released power in the past. A high discharge frequency has a certain impact on the unit. L is the remaining lifetime, which indicates how long the energy storage unit can still operate normally. A longer remaining lifetime usually means a more stable demand response capability. T is the unit's power supply duration, which directly relates to the energy storage unit's continuous ability to meet power demand. The longer the power supply duration, the better the demand response capability may be.

[0092] This function comprehensively considers multiple factors such as quantitative health status, historical charging frequency, historical discharging frequency, remaining lifespan, and unit power supply duration to evaluate the energy storage unit's responsiveness to demand, providing a scientific basis for prioritizing charging tasks and dynamically adjusting charging power.

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

[0094] Step S741: Negatively serialize the multiple demand response indices to sort the discharge tasks of the multiple energy storage units.

[0095] Step S742: Using the stable window identifier as a constraint, the power of the multiple energy storage units is dynamically adjusted to discharge to the outside according to the real-time discharge demand, based on the discharge task order.

[0096] Specifically, a negative serialization operation is performed on multiple demand response indices. Negative serialization means arranging the demand response indices in ascending order. This result is used to prioritize the discharge tasks of multiple energy storage units. Energy storage units with lower demand response indices have higher priority in the discharge task ranking because they tend to perform better in certain aspects, such as higher health and longer remaining lifespan, making them suitable for priority discharge. This method allows for a more rational arrangement of the discharge sequence of energy storage units, improving the overall system's operating efficiency and stability.

[0097] Using a stable window identifier as a constraint, multiple energy storage units are scheduled based on the previously obtained discharge task sequence. The stable window identifier limits a specific time range or other relevant conditions to ensure that the discharge operation is carried out within an appropriate range. Based on the discharge task sequence, energy storage units with lower demand response indices are prioritized for discharge. Simultaneously, the current power requirement for external discharge is determined by considering real-time discharge demand. The external discharge power of multiple energy storage units is dynamically adjusted according to real-time discharge demand. For example, when the real-time discharge demand is high, the discharge power of the energy storage units is increased; when the demand is low, the discharge power is reduced accordingly. The status and discharge situation of each energy storage unit are monitored in real time, and the discharge power of the energy storage units is controlled sequentially according to the discharge task sequence to meet real-time discharge demand. This ensures that the discharge process of the energy storage units is efficient and stable, and can be flexibly adjusted according to actual needs, improving the energy utilization efficiency and reliability of the entire system.

[0098] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

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

[0100] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for dynamically adjusting charging and discharging power based on demand response, characterized in that, The method includes: Historical data of the target power pool is retrieved to obtain historical discharge data, wherein the target power pool integrates multiple energy storage units; Based on the historical discharge data, discharge demand analysis is performed to construct a discharge demand analysis model. Data is collected from the power supply area under preset environmental conditions to obtain stable environmental data, wherein the stable environmental data has a stable window identifier; Based on the stable window identifier, the power supply mode is identified to obtain the real-time power supply mode; The stable environment data and real-time power supply mode are synchronized to the discharge demand analysis model to obtain the real-time discharge demand. The real-time discharge amount is calculated based on the stable window identifier and the real-time discharge requirement, and it is determined 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, then 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 method further includes performing discharge demand analysis based on the historical discharge data to construct a discharge demand analysis model. The historical discharge data is segmented based on the season to obtain discharge data for multiple seasons. During the preset seasonal transition period, discharge data of adjacent seasons are sampled, and based on the sampling results, the multiple seasonal discharge data are mapped and overlapped to obtain multiple reliable discharge data. Based on the first reliable discharge data segmented by discharge time, reliable daytime discharge data and reliable nighttime discharge data are obtained, wherein the first reliable discharge data is any one of the plurality of reliable discharge data; The reliable daytime discharge data was disassembled to obtain multiple sample discharge power and multiple sample environmental data. The random operator is called from the operator library, and the obtained random operator is trained and optimized using the discharge power of the multiple samples and the environmental data of the multiple samples to obtain the daytime power analysis sub-branch; Similarly, a nighttime power analysis sub-branch is constructed based on the aforementioned reliable nighttime discharge data; The daytime power analysis sub-branch and the nighttime power analysis sub-branch are connected in parallel to obtain the first power prediction branch, and the first power prediction branch is identified by the first season. By analogy, multiple power prediction branches are constructed, and the discharge demand analysis model is completed by connecting the multiple power prediction branches in parallel. The method for identifying the power supply mode based on the stable window identifier to obtain the real-time power supply mode further includes: Determine whether the stable window identifier falls within the seasonal transition period. If the stable window identifier falls within the seasonal transition period, then generate the first transition season and the second transition season. Based on the stable window identifier, the discharge time is determined 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; If the remaining power of the target power pool does not meet the real-time discharge requirement, then tasks are allocated to the plurality of energy storage units, and the charging power of the target power pool is adjusted based on the task allocation results. The method further includes: Based on the real-time discharge demand and multiple remaining energy storage units, the power supply duration of multiple units is calculated. The system interactively obtains multiple unit states of the multiple energy storage units, wherein each unit state includes quantified health, historical charging frequency, historical discharging frequency, and remaining service life. A pre-constructed demand response quantification function is used, and multiple demand response indices are obtained by synchronizing the power supply duration and status of the multiple units to the demand response quantification function. The multiple demand response indices are forward-serialized and used as the order of charging tasks for the multiple energy storage units; Multiple charging power constraints of the multiple energy storage units are obtained interactively; The multiple energy storage units are scheduled according to the charging task order, and the charging power of the energy storage units is dynamically adjusted according to the multiple charging power constraints.

2. The method for dynamic adjustment of charging and discharging power based on demand response as described in claim 1, characterized in that, The environmental acquisition conditions include acquisition triggering conditions and acquisition suppression conditions.

3. The method for dynamic adjustment of charging and discharging power based on demand response as described in claim 1, characterized in that, The method further includes synchronizing the stable environment data and real-time power supply mode to the discharge demand analysis model to obtain real-time discharge demand. Based on the seasonal identifier mapping, activate the first real-time prediction branch in the discharge demand analysis model corresponding to the first transition season and the second real-time prediction branch corresponding to the second transition season; Based on 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. The stable environment data is synchronized to the first power analysis sub-branch and the second power analysis sub-branch for power prediction, and the first real-time prediction result and the second real-time prediction result are obtained. The real-time discharge requirement is obtained by averaging the first real-time prediction result and the second real-time prediction result.

4. The method for dynamic adjustment of charging and discharging power based on demand response as described in claim 1, characterized in that, The method further includes: If the remaining power of the target power pool meets the real-time discharge amount, then the electricity price is called with the stability window identifier as a constraint to obtain the real-time electricity price sequence; The charging cycle time of the target power pool is obtained interactively, and the window of the stable window identifier is updated by using 1 / 3 of the charging cycle time to obtain the updated window identifier. The electricity price is called with the updated window identifier as a constraint to obtain the updated electricity price sequence. If the updated electricity price sequence is better than the real-time electricity price sequence, then the remaining energy storage of the multiple energy storage units is obtained interactively, and a charging scheduling sequence is generated based on the multiple remaining energy storage. At the start time indicated by the update window, the charging management of the multiple energy storage units is performed according to the charging scheduling sequence.

5. The method for dynamic adjustment of charging and discharging power based on demand response as described in claim 1, characterized in that, The demand response quantization function is as follows: ; in, This is the demand response index. To quantify health status, Historical charging frequency, Historical discharge frequency For the remaining service life, The duration of power supply to the unit.

6. The method for dynamic adjustment of charging and discharging power based on demand response as described in claim 1, characterized in that, The method further includes: The multiple demand response indices are negatively serialized and used as the order of discharge tasks for the multiple energy storage units; Using the stable window identifier as a constraint, the power of the multiple energy storage units is dynamically adjusted according to the real-time discharge demand based on the discharge task order.

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