Method and system for improving power supply potential of emerging load

Abstract

The invention relates to the technical field of power supply, in particular to an emerging load power supply potential improvement method and system, and the method comprises the following steps: obtaining power demand data of a user in a plurality of time periods, calculating a load change rate in each time period, constructing a load behavior mode feature set, and obtaining load behavior mode feature data. According to the invention, by analyzing the user power demand data and calculating the real-time power demand comparison mapping information, the power demand can be effectively identified and adjusted, the coping capability and accuracy of power supply are improved, and by dynamically adjusting the discharge priority and time distribution of the energy storage unit, the power supply efficiency is improved. The use efficiency and response speed of energy storage equipment are improved, peak load shifting is facilitated, high efficiency and safety of power grid operation are kept, the overall performance and reliability of a power system are improved, the emergency response capacity of the power system to emergencies is enhanced, and the supply potential of power is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply, and in particular to a method and system for improving the power supply potential of emerging loads. Background Art

[0002] The field of power supply technology mainly involves the generation, transmission, distribution and end-use of electricity. This technical field includes the design and optimization of power systems, the stability and reliability analysis of power grids, and technologies and methods related to improving the efficiency of power use by end users. The core goal of power supply is to ensure the efficient operation of the power system and the continuous supply of energy while reducing energy loss and environmental impact. This field also includes the integration of new energy systems and technologies to improve the efficiency of energy storage and conversion.

[0003] Among them, the method of improving the power supply potential of emerging loads aims to enhance the ability of the power system to cope with high load demands by adopting advanced power management and dispatching technologies. The method usually involves optimizing the operation mode of the power grid using intelligent algorithms, improving the allocation and utilization efficiency of energy resources to cope with peak loads and reduce grid pressure. In addition, the method also includes the development of new load response technologies and strategies to promote the dynamic balance of power supply and demand and improve the overall performance and reliability of the system. The application of these technologies and methods helps to promote energy conservation and environmental protection, while improving the economy and safety of the power supply system.

[0004] Traditional supply methods rely on load forecasting models and fail to fully consider the diversity and randomness of user behavior, making it difficult to accurately respond to demand fluctuations in actual operations. Traditional methods also show shortcomings in resource allocation and utilization efficiency, especially when electricity demand suddenly increases. The response speed and adjustment capabilities of traditional methods are insufficient, resulting in uneven grid load and energy waste. For example, if energy storage equipment is not effectively utilized, the grid will suffer from the risk of insufficient power supply during high-load periods, affecting the reliability of power supply and users' power experience, limiting the potential of the power system in energy efficiency optimization and cost control, resulting in energy waste and increased environmental burden, and affecting the overall performance and sustainable development of the power system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a method and system for improving the power supply potential of emerging loads.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for improving the power supply potential of emerging loads, comprising the following steps: S1: Obtain the power demand data of the user in multiple time periods, calculate the load change rate in each time period, construct a load behavior pattern feature set, and obtain load behavior pattern feature data; S2: Based on the load behavior pattern characteristic data, the similarity score between the current data and the known load pattern is calculated, and the load change rate of the adjacent time period is called to calculate the adjustment amount of the current power demand, and obtain the real-time power demand comparison mapping information; S3: calling the real-time power demand comparison mapping information, screening the time period when the current load demand change amplitude exceeds the load fluctuation allowable range, and correcting the load acceptable power supply adjustment time range in combination with the change trend of the load operation status to obtain the load dynamic response window; S4: calling the load dynamic response window, obtaining the current available capacity of the energy storage device, calculating the discharge priority of the energy storage unit according to the current state of the energy storage unit, and generating the peak shaving discharge sequence of the energy storage cluster; S5: calling the peak shaving discharge sequence of the energy storage cluster, calculating the energy storage discharge amount required during the peak load period, adjusting the discharge duration of the energy storage unit, and obtaining the energy storage scheduling adjustment parameters.

[0007] As a further solution of the present invention, the load behavior pattern characteristic parameters include daily load fluctuation amplitude, weekly load balancing coefficient, monthly load growth ratio, load change rate, and power demand stability; the real-time power demand comparison mapping information specifically includes power demand mapping value, load matching threshold, similarity score, interpolation calculation optimization value, and adjacent load pattern parameters; the load dynamic response window parameters include load starting power, stable operating power, load allowable fluctuation range, power supply adjustment window duration, and load fluctuation rate; the load priority parameters specifically include load category, grid load margin, equipment power demand, response priority setting value, and load adjustment priority; the energy storage cluster discharge optimization strategy includes the remaining available capacity of the energy storage system, the current grid load level, the energy storage unit discharge power, the discharge strategy setting value, and the discharge sequence adjustment value.

[0008] As a further solution of the present invention, the step of acquiring the load behavior pattern characteristic data is specifically as follows: S111: obtaining power demand data of the user in multiple time periods, extracting the load fluctuation amplitude, load balancing coefficient and load growth ratio in each time period, calculating the load change rate in each time period, and calculating the standard deviation of the load change in multiple time periods according to the load change rate to obtain the load change dispersion; S112: Based on the load variation dispersion, determine the stability of the power demand data, classify the stability into multiple levels, and combine the load variation rate of each level to adopt the formula: ; The stability deviation value is obtained by calculation, and the power demand stability deviation coefficient is obtained; in, represents the power demand stability deviation coefficient, Representative The load change rate in a time period, represents the average load change rate over a time period, Represents the total number of time periods; S113: Based on the power demand stability deviation coefficient, the change trends of the load fluctuation amplitude, the load balancing coefficient and the load growth ratio are extracted, a load behavior pattern feature set is constructed, and load behavior pattern feature data is obtained.

[0009] As a further solution of the present invention, the step of acquiring the real-time power demand comparison mapping information is specifically as follows: S211: Based on the load behavior pattern characteristic data, the actual load data of the user in the current time window is obtained, using the formula: ; The matching degree between the actual load data and the known load pattern is calculated to obtain the load similarity score; in, represents the load similarity score, Represents the current Actual data of load characteristics, Represents a known load pattern The corresponding data of the load characteristics, represents the total number of load characteristics; S212: Based on the load similarity score, determine whether the score is lower than the load matching threshold. If it is lower than the threshold, call the load change rate of the adjacent time period, calculate the average change rate of each time period, and obtain the adjustment amount of the current power demand to obtain the real-time power demand adjustment value; S213: Based on the real-time power demand adjustment value, calculate the offset degree of the current power demand mapped to the load behavior pattern, compare it with the known load pattern, calculate the adjusted load change trend, and obtain the real-time power demand comparison mapping information.

[0010] As a further solution of the present invention, the step of obtaining the load dynamic response window is specifically as follows: S311: Obtain the real-time power demand comparison mapping information, extract the current load state parameters, obtain the user-side load starting power and stable operating power, calculate the load allowable fluctuation range, adjust the load fluctuation upper and lower limits according to the power demand characteristics of the load equipment, and obtain the load power fluctuation parameters; S312: Call the load power fluctuation parameter to obtain the load operation time, load fluctuation amplitude, and user-acceptable load adjustment time, using the formula: ; Calculate and obtain the power supply adjustment time range, and generate power supply adjustment window parameters; in, Represents the duration of the power supply adjustment window. Represents stable operating power, Represents the load starting power, Represents the load adjustment response rate, represents the critical value of user load demand, Representative Adjustable power factor of each load unit, Represents the total number of load units used for calculation; S313: calling the power supply adjustment window parameters, obtaining the load fluctuation rate, calculating the dynamic correction ratio of the current power supply adjustment window, adjusting the upper limit and the lower limit of the power supply adjustment window, and obtaining the load dynamic response window.

[0011] As a further solution of the present invention, the steps for obtaining the peak shaving discharge sequence of the energy storage cluster are specifically as follows: S411: calling the load dynamic response window, obtaining the current available capacity of the energy storage device, calculating the load change rate per unit time, screening the discharge power, remaining discharge time, and charge and discharge efficiency of each energy storage unit, calculating and obtaining the state parameters of each energy storage unit, and obtaining the discharge capacity evaluation value of the energy storage unit; S412: Based on the evaluation value of the discharge capacity of the energy storage unit, combined with the discharge power of the energy storage unit and the load demand power, the formula is adopted: ; Obtaining the priority ranking value of each energy storage unit by calculation, and obtaining the discharge priority of the energy storage unit; in, Represents the discharge priority of the energy storage unit, represents the current available capacity of the energy storage unit, Represents the charging and discharging efficiency of the energy storage unit, Represents the remaining discharge time of the energy storage unit. Represents the maximum discharge power of the energy storage unit, Represents the power required by the current load; S413: Based on the energy storage unit discharge priority, the energy storage power release sequence is set from high to low priority, the energy storage unit power scheduling under each load state is matched, and the peak shaving discharge sequence of the energy storage cluster is obtained.

[0012] As a further solution of the present invention, the step of obtaining the energy storage scheduling adjustment parameter is specifically as follows: S511: calling the peak shaving discharge sequence of the energy storage cluster, calculating the required energy storage discharge amount during the peak load period, and screening the energy storage units that can be used for scheduling in combination with the current discharge power, health status and temperature change rate of each energy storage unit to obtain the dispatchable capacity evaluation value of the energy storage unit; S512: Based on the dispatchable capacity evaluation value of the energy storage unit, combined with the health status, temperature change rate and current discharge power of the energy storage unit, the formula is used: ; Calculate and obtain the energy storage unit discharge time distribution value; in, Represents the discharge time distribution value of the energy storage unit, represents the current available capacity of the energy storage unit, Represents the preset minimum safety capacity of the energy storage unit. Represents the charging and discharging efficiency of the energy storage unit, Represents the health status influencing factor of the energy storage unit, represents the discharge power of the energy storage unit, Represents the temperature change rate influence coefficient of the energy storage unit; S513: Based on the energy storage unit discharge time allocation value, adjust the discharge time of the energy storage unit, optimize the discharge time allocation of the energy storage unit, and establish energy storage scheduling adjustment parameters.

[0013] A system for improving the power supply potential of emerging loads, the system for improving the power supply potential of emerging loads is used to execute the above-mentioned method for improving the power supply potential of emerging loads, and the system comprises: The load characteristic identification module obtains the power demand data of the user in multiple time periods, calculates the load change rate in each time period, constructs a load behavior pattern characteristic set, and obtains load behavior pattern characteristic data; The power demand comparison module calculates the similarity score between the current data and the known load pattern based on the load behavior pattern characteristic data, then calls the load change rate of the adjacent time period, calculates the adjustment amount of the current power demand, and obtains the real-time power demand comparison mapping information; The power supply time adjustment module calls the real-time power demand comparison mapping information, corrects the acceptable power supply adjustment time range of the load, and obtains the load dynamic response window; The discharge sequence optimization module calls the load dynamic response window, obtains the current available capacity of the energy storage device, calculates the discharge priority of the energy storage unit, and generates the peak shaving discharge sequence of the energy storage cluster; The discharge parameter adjustment module calls the peak shaving discharge sequence of the energy storage cluster, calculates the energy storage discharge amount required during the peak load period, adjusts the discharge duration of the energy storage unit, and obtains the energy storage scheduling adjustment parameters.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by analyzing the power demand data of users and calculating the real-time power demand comparison mapping information, the power demand can be effectively identified and adjusted, the management of the load demand variation range is optimized, the stability of the power grid and the balance of the load are ensured when the demand fluctuates sharply, the response capability and accuracy of the power supply are improved, and the use efficiency and response speed of the energy storage equipment are improved by dynamically adjusting the discharge priority and time allocation of the energy storage unit, which helps to reduce peak loads and fill valleys, maintain the efficiency and safety of the power grid operation, improve the overall performance and reliability of the power system, reduce the energy waste caused by load fluctuations, enhance the emergency response capability of the power system to emergencies, and optimize the load power supply potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 A flow chart for obtaining load behavior pattern characteristic data of the present invention; Figure 3 A flowchart of obtaining real-time power demand comparison mapping information for the present invention; Figure 4 A flow chart for obtaining a load dynamic response window according to the present invention; Figure 5 A flow chart for obtaining a peak shaving discharge sequence of an energy storage cluster for the present invention; Figure 6 A flow chart of obtaining energy storage scheduling adjustment parameters for the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] Embodiment 1 See also Figure 1 The present invention provides a technical solution: a method for improving the power supply potential of emerging loads, comprising the following steps: S1: Obtain the power demand data of users in multiple time periods, extract the load fluctuation amplitude, load balancing coefficient and load growth ratio in each time period, calculate the load change rate in each time period, and construct a load behavior pattern feature set according to the stability of the user's power demand to obtain the load behavior pattern feature data; S2: Based on the load behavior pattern characteristic data, the actual load data of the user in the current time window is obtained, and the similarity score between the current data and the known load pattern is calculated. It is determined whether the similarity score is lower than the load matching threshold, and the load change rate of the adjacent time period is called to calculate the adjustment amount of the current power demand, and the real-time power demand comparison mapping information is obtained; S3: Call the real-time power demand comparison mapping information, filter the time period when the current load demand change range exceeds the load fluctuation allowable range, calculate the load startup demand power, stable operation power, and allowable delay window, and correct the load acceptable power supply adjustment time range in combination with the change trend of the load operation status to obtain the load dynamic response window; S4: Call the load dynamic response window to obtain the current available capacity of the energy storage device, calculate the load change rate per unit time, screen the discharge power, remaining discharge time, and charge and discharge efficiency of the energy storage unit, calculate the discharge priority of the energy storage unit according to the current state of the energy storage unit, set the energy storage power release order according to the discharge priority, and generate the peak shaving discharge order of the energy storage cluster; S5: Call the peak shaving discharge sequence of the energy storage cluster, calculate the energy storage discharge required during the peak load period, adjust the discharge duration of the energy storage unit according to the health status, temperature change rate and current discharge power of the energy storage unit, optimize the discharge time allocation of the energy storage unit, and obtain the energy storage scheduling adjustment parameters; The characteristic parameters of load behavior patterns include daily load fluctuation amplitude, weekly load balancing coefficient, monthly load growth ratio, load change rate, and power demand stability; the real-time power demand comparison mapping information includes power demand mapping value, load matching threshold, similarity score, interpolation calculation optimization value, and adjacent load pattern parameters; the load dynamic response window parameters include load starting power, stable operating power, load allowable fluctuation range, power supply adjustment window duration, and load fluctuation rate; the load priority parameters include load category, grid load margin, equipment power demand, response priority setting value, and load adjustment priority; the energy storage cluster discharge optimization strategy includes the remaining available capacity of the energy storage system, the current grid load level, the energy storage unit discharge power, the discharge strategy setting value, and the discharge sequence adjustment value.

[0019] See also Figure 2, the steps for obtaining the characteristic data of load behavior pattern are as follows: S111: obtaining power demand data of the user in multiple time periods, extracting the load fluctuation amplitude, load balancing coefficient and load growth ratio in each time period, calculating the load change rate in each time period, and calculating the standard deviation of the load change in multiple time periods according to the load change rate to obtain the load change dispersion; After obtaining the power demand data of the user in multiple time periods, it is necessary to process the load data in each time period and extract the load fluctuation amplitude of the time period. The parameter can be obtained by calculating the difference between the maximum load value and the minimum load value in the period. For example, if the maximum load value in a certain period is 120kW and the minimum load value is 80kW, then the load fluctuation amplitude of the period is 40kW; secondly, calculate the load balancing coefficient of the period. The coefficient can be obtained by dividing the load mean value in the time period by the maximum load value. For example, if the load data of a certain period are 90kW, 100kW, and 110kW respectively, the load mean value is 100kW, and the corresponding load balancing coefficient is calculated as ; Calculate the load growth ratio, which can be defined as the ratio of the maximum load of this cycle to the maximum load of the previous cycle. If the maximum load of the previous cycle is 100kW, then the load growth ratio of this cycle is Based on the above calculation results, calculate the load change rate in each time period. The rate can be obtained by calculating the change of the load mean value in this time period compared with the load mean value of the previous period divided by the load mean value of the previous period. For example, if the load mean value of the previous period is 95kW, then the load change rate of this period is ; After obtaining the load change rate in all time periods, calculate the standard deviation of the load change in multiple time periods. The standard deviation is calculated using the standard deviation formula. The specific calculation formula is: ,in, is the load variation standard deviation, For the The load change rate in a time period, is the average value of the load change rate in all time periods, is the total number of time periods, and the load change dispersion is obtained. For example, if the load change rates in a certain area are 0.05, 0.07, and 0.09 respectively, then the mean load change rate , the standard deviation is calculated as , which is the load variation dispersion. The reference value of load variation dispersion is set based on the standard deviation range under stable load conditions in historical data. Under stable working conditions, this value is usually between 0.01-0.03. If it exceeds 0.05, it indicates that the load changes drastically. Therefore, 0.02 is set as the reference value of load variation dispersion as a preliminary judgment standard for load stability. If the value is lower than 0.02, it means that the load is relatively stable. If it is between 0.02-0.05, it indicates that the load has moderate fluctuations. If it exceeds 0.05, the load fluctuates significantly.

[0020] S112: Based on the load variation dispersion, the stability of the power demand data is determined, and the stability is classified into multiple levels. Combined with the load variation rate of each level, the formula is used: ; The stability deviation value is obtained by calculation, and the power demand stability deviation coefficient is obtained; in, represents the power demand stability deviation coefficient, Representative The load change rate in a time period, represents the average load change rate over a time period, Represents the total number of time periods; Based on the calculated load variation dispersion, the stability of the power demand data is judged. First, the stability classification level needs to be set. For example, the load variation dispersion in the range of 0-0.02 can be classified as high stability, in the range of 0.02-0.05 can be classified as medium stability, and above 0.05 can be classified as low stability. After determining the stability level, the stability deviation value is further calculated in combination with the load change rate of each level. The calculation adopts the formula: ; in, is the power demand stability deviation coefficient, For the The load change rate in a time period, is the average load change rate in all time periods, is the total number of time periods. The specific calculation process is as follows: if the load change rate of a system in 5 time periods is 0.05, 0.06, 0.07, 0.08, and 0.09 respectively, then its average , enter the formula for calculation: ; Get the power demand stability deviation coefficient , if the classification threshold is set to 0.02, the power demand stability of the system is medium stability. The classification threshold of the stability deviation coefficient is based on the historical data analysis under different power demand modes. Under long-term high-load operation, the typical value of D is usually 0.01-0.03. If it exceeds 0.03, it indicates that the system has large power fluctuations. Therefore, 0.02 is set as the critical value of power stability classification, so that when the D value is lower than 0.02, it can be judged as a high stability state, between 0.02-0.05 is medium stability, and more than 0.05 is low stability.

[0021] S113: Based on the power demand stability deviation coefficient, the change trends of the load fluctuation amplitude, the load balancing coefficient and the load growth ratio are extracted, a load behavior pattern feature set is constructed, and load behavior pattern feature data is obtained.

[0022] Based on the calculated power demand stability deviation coefficient, the change trends of load fluctuation amplitude, load balancing coefficient and load growth ratio are extracted. By calculating the change rate of parameters in each time period, a load behavior pattern feature set is formed. For example, in the past five time periods, the load fluctuation amplitudes are 40kW, 42kW, 45kW, 50kW and 55kW respectively, and the change rate is calculated as follows: ; in, For the The rate of change of load fluctuation amplitude in a time period, For the The load fluctuation amplitude of a time period is calculated as: ; ; ; ; The weight setting of the load fluctuation amplitude change rate is based on the impact of historical load data fluctuations. In scenarios with high power supply reliability requirements, if the load fluctuation amplitude change rate exceeds 0.1, it may affect system stability, so its weight is set to 0.6. When it is less than 0.05, the impact on the system is small, and the weight is set to 0.3. The middle value is set to 0.45. As shown in Table 1, by calculating the load parameters and their change trends in each time period, the load behavior pattern characteristic data can be formed to provide a reference for subsequent load prediction and optimization.

[0023] See also Figure 3 ,The specific steps for obtaining the real-time power demand comparison mapping information are as follows: S211: Based on the load behavior pattern characteristic data, the actual load data of the user in the current time window is obtained, using the formula: ; The matching degree between the actual load data and the known load pattern is calculated to obtain the load similarity score; in, represents the load similarity score, Represents the current Actual data of load characteristics, Represents a known load pattern The corresponding data of the load characteristics, represents the total number of load characteristics; Based on the load behavior pattern characteristic data, the actual load data of the user in the current time window is obtained. In this time window, the current load characteristic data is first read. The data contains parameters such as the load fluctuation amplitude, load balancing coefficient and load growth ratio of the current cycle. After reading, the parameters are normalized to eliminate the influence of units and orders of magnitude. The normalization method adopts linear normalization, that is, the formula is used Normalize the load parameters. For example, if the load fluctuation amplitude in a certain period is 50kW, and the minimum and maximum values ​​of the parameter in the historical period are set to 40kW and 60kW respectively, the normalized result is , the remaining load parameters are normalized in the same way and matched with the known load pattern. The matching degree is calculated using the formula: ; in, represents the load similarity score, For the current Actual data of load characteristics, For a known load pattern The corresponding data of the load characteristics, is the total number of load characteristics. The load characteristics are set to include load fluctuation amplitude, load balancing coefficient and load growth ratio. If the current actual load characteristics are , the corresponding characteristic value of the known load mode is , the similarity score is calculated as follows: ; Calculate the load similarity score , which is used to evaluate the matching degree between the actual load data in the current time window and the known load pattern. As a normalized benchmark value, it is calculated based on the historical load pattern characteristics. The range of the benchmark value needs to cover more than 90% of the historical samples to avoid excessively high or low normalization leading to calculation distortion. Taking the historical load characteristic statistical data of a certain region as an example, the benchmark value usually falls within to In this calculation, 1.95 is used.

[0024] S212: Based on the load similarity score, determine whether the score is lower than the load matching threshold. If it is lower than the threshold, call the load change rate of the adjacent time period, calculate the average change rate of each time period, and obtain the adjustment amount of the current power demand to obtain the real-time power demand adjustment value; Based on the load similarity score, determine whether the score is lower than the load matching threshold. First, set the load matching threshold The threshold is set based on the stability of the historical load pattern and matching requirements, for example, , if the calculated load similarity score , then it satisfies , no need to adjust the load demand; if the load similarity score calculated at a certain moment , then it satisfies , the power demand needs to be adjusted. First, the load change rate of adjacent time periods is called, and the average change rate of each time period is calculated. The load change rates of the first five time periods are set to , the average rate of change is calculated as follows: ; Based on the rate of change, the adjustment amount of the current power demand is calculated, which is defined as ,in is the current load average value, assuming that the current load average value is 100kW, the adjustment amount is calculated as follows: ; That is, the current power demand needs to be increased by 5kW to make the load characteristics closer to the known load pattern, and finally obtain the real-time power demand adjustment value , load matching threshold The setting of is based on the system's tolerance for load errors. This value is usually taken as the 95% quantile of the historical average matching degree to ensure that more than 90% of the load matching degrees are above the threshold. For example, the 95% quantile of the average matching degree of a region in the past year is 0.91, and the value is taken as 0.9 this time. The reasonable range is usually set to .

[0025] S213: Based on the real-time power demand adjustment value, calculate the offset degree of the current power demand mapped to the load behavior pattern, compare it with the known load pattern, calculate the adjusted load change trend, and obtain the real-time power demand comparison mapping information.

[0026] Based on the real-time power demand adjustment value, calculate the degree of deviation of the current power demand mapped to the load behavior pattern. First, calculate the adjusted load mean , bring in the aforementioned data: ; Based on the adjusted load mean, calculate the degree of deviation relative to the known load pattern, using the deviation calculation formula ,in is the mean of the historical load pattern. Assuming the mean of the historical load pattern is 102kW, the calculation deviation is as follows: ; The current power demand is offset by 0.0294 relative to the known load pattern. By comparing with the known load pattern, the load change trend after adjustment is calculated, and the load change rate of each time period is calculated. , after adjustment, the load change rates in each time period are , calculate the adjusted mean: ; Based on the adjusted load change trend, the real-time power demand comparison mapping information is constructed, as shown in Table 1.

[0027] Table 1 Real-time power demand comparison mapping information As shown in Table 1, the load change rate after adjustment is higher than that before adjustment, and the offset degree remains stable. In the load change rate comparison calculation before and after adjustment, the load change rate weight coefficient adopts equal weight, that is, the rate influence of each time period is The weight setting is based on the time continuity of the load pattern. There is no significant trend change in a short time period, so equal weight is adopted.

[0028] See also Figure 4 , the specific steps for obtaining the load dynamic response window are: S311: Obtain real-time power demand comparison mapping information, extract current load state parameters, obtain user-side load startup power and stable operating power, calculate the load allowable fluctuation range, adjust the load fluctuation upper and lower limits according to the power demand characteristics of the load equipment, and obtain load power fluctuation parameters; After obtaining the real-time power demand comparison mapping information, extract the current load state parameters, read the operating data of the user's current load equipment, and record the load starting power and stable operating power respectively. The load starting power refers to the instantaneous power required for the equipment to enter the normal operating state from the static state, and the stable operating power is the constant power required for the equipment to maintain operation after entering the normal operating state. For example, the starting power of an industrial equipment is 150kW, and the stable operating power is 100kW. After obtaining these parameters, calculate the load allowable fluctuation range, which is defined as ,in Indicates the maximum power allowed by the load equipment during operation. Indicates the minimum power allowed by the load equipment. For example, if the maximum power of the equipment is set to 110kW and the minimum power is set to 90kW, the allowable load fluctuation range is calculated as follows: ; Based on the calculated load fluctuation range, adjust the load fluctuation upper and lower limits. If the load adjustment strategy of a certain device allows a fluctuation of 10%, calculate the adjusted fluctuation upper and lower limits and set the adjustment ratio. is 10%, calculated as follows: ; ; Get load power fluctuation parameters , where the adjustment ratio The setting is based on the statistical data of the equipment's operating load fluctuations. This value mainly changes with the equipment's load rate. When the equipment's load rate is between 60% and 80%, the load fluctuation that the equipment can withstand is generally allowed to be between 8% and 12%. Therefore, the setting , this value decreases as the equipment load rate increases. For example, when the equipment load rate is higher than 90%, the adjustment ratio can be reduced to 5%-7% to reduce the risk of equipment overload operation.

[0029] S312: Call the load power fluctuation parameter to obtain the load operation time, load fluctuation amplitude, and user-acceptable load adjustment time using the formula: ; Calculate and obtain the power supply adjustment time range, and generate power supply adjustment window parameters; in, Represents the duration of the power supply adjustment window. Represents stable operating power, Represents the load starting power, Represents the load adjustment response rate, represents the critical value of user load demand, Representative Adjustable power factor of each load unit, Represents the total number of load units used for calculation; Call the load power fluctuation parameters to obtain the load operation time, load fluctuation amplitude, and user-acceptable load adjustment time. First, read the load operation time. This parameter can be obtained from the user's load operation history data. For example, if the total operation time of a device is 8 hours, the load operation time is 480 minutes. On this basis, calculate the load fluctuation amplitude, which is defined as , known , calculate the load fluctuation amplitude as follows: ; Then obtain the load adjustment time acceptable to the user. This time is usually set according to the user's response requirements for load adjustment. For example, if a factory allows a load adjustment time window of 15 minutes, then this value is set as Minutes, after obtaining these parameters, use the formula: ; Calculate the power supply adjustment time range, where (Stable operating power), (Load starting power), set the load adjustment response rate , user load demand critical value , load unit can adjust power factor Take [2, 3, 4]kW respectively, the total number of load units , calculated as follows: ; After adjustment, the power supply adjustment window parameters are obtained Minutes, of which, load adjustment response rate The setting of is based on the load response time and load adjustment requirements of the equipment. This value varies with the operating status of the equipment. When the load is low, the equipment adjustment response time is faster, so It can be set to 0.6. However, under high load operation, the adjustment response time is longer. Generally, the value is 0.5-0.7 to avoid excessive load adjustment which may cause sudden load changes on the equipment and affect operational stability.

[0030] S313: calling the power supply adjustment window parameters, obtaining the load fluctuation rate, calculating the dynamic correction ratio of the current power supply adjustment window, adjusting the upper and lower limits of the power supply adjustment window, and obtaining the load dynamic response window; Call the power supply adjustment window parameters, obtain the load fluctuation rate, and calculate the dynamic correction ratio of the current power supply adjustment window. First, calculate the load fluctuation rate, which is defined as , known , Minutes (set to absolute value after adjustment), calculated as follows: ; After calculating the load fluctuation rate, the dynamic correction ratio of the current power supply adjustment window is calculated. The correction ratio is used to adjust the upper and lower limits of the power supply adjustment window. The calculation method of the correction ratio is: ,in Minutes, calculated as follows: ; The correction ratio indicates that the deviation ratio between the current power supply adjustment window and the user's acceptable load adjustment time is 0.75, and correction is needed to adjust the upper and lower limits of the power supply adjustment window, and calculate the corrected power supply adjustment window range: ; ; Finally, the load dynamic response window is obtained Minutes, of which the dynamic correction ratio The setting is based on the adjustment time error range, which is mainly affected by the user's acceptable adjustment time and the adjustment capacity of the power supply system. Usually, when the deviation between the power supply adjustment window and the user's adjustment demand is greater than 50%, the correction ratio needs to be adjusted to the range of 0.7-0.9. When the deviation is less than 50%, the correction ratio is set to 0.3-0.5 to ensure that the adjusted power supply window meets the user's load response requirements.

[0031] See also Figure 5 , the specific steps for obtaining the peak shaving discharge sequence of the energy storage cluster are: S411: calling the load dynamic response window, obtaining the current available capacity of the energy storage device, calculating the load change rate per unit time, screening the discharge power, remaining discharge time, and charge and discharge efficiency of each energy storage unit, calculating and obtaining the state parameters of each energy storage unit, and obtaining the discharge capacity evaluation value of the energy storage unit; After calling the load dynamic response window, first obtain the current available capacity of the energy storage device, which refers to the amount of electricity that the energy storage device can be used for discharge at the current moment, and record the current remaining storage energy of each energy storage unit. For example, if the current available capacity of a certain energy storage unit is 50kWh, then calculate the load change rate per unit time. The rate is defined as the ratio of the load power change to the time interval. For example, in the past 5 minutes, the load power increased from 100kW to 120kW, and the load change rate is calculated as follows: ; After calculating the load change rate, the discharge power, remaining discharge time and charge and discharge efficiency of each energy storage unit are screened. The discharge power refers to the power that the energy storage unit can provide under the maximum discharge state. For example, the maximum discharge power of a certain energy storage unit is 30kW, and the remaining discharge time is defined as the ratio of the current available capacity to the discharge power. For example, if the current available capacity of the energy storage unit is 50kWh and the discharge power is 30kW, the remaining discharge time is calculated as follows: ; The charge and discharge efficiency indicates the energy loss rate during the charge and discharge process. Assuming that the charge and discharge efficiency of a certain energy storage unit is 90% (i.e. 0.9), after obtaining the above parameters, the state parameters of each energy storage unit are calculated. The parameters are normalized based on the current available capacity, discharge power, remaining discharge time and charge and discharge efficiency of the energy storage unit to standardize the capacity indicators of different energy storage units. For example, the value range of the normalized state parameters is set to [0,1], and the discharge capacity evaluation value of the energy storage unit is finally calculated. For the discharge efficiency ( ), the specific setting is based on the technical specifications of the energy storage equipment and the energy loss during the actual discharge process. It is generally based on the rated conversion efficiency of the equipment and is calculated in combination with historical operating data. The setting value is usually between 0.85 and 0.95. For example, the nominal value of the charge and discharge efficiency of a lithium iron phosphate battery energy storage unit is 92%, but in actual operation, due to the influence of battery internal resistance and temperature changes, there will be about 2% additional loss, so the actual setting .

[0032] S412: Based on the evaluation value of the discharge capacity of the energy storage unit, combined with the discharge power of the energy storage unit and the load demand power, the formula is used: ; Obtaining the priority ranking value of each energy storage unit by calculation, and obtaining the discharge priority of the energy storage unit; in, Represents the discharge priority of the energy storage unit, represents the current available capacity of the energy storage unit, Represents the charging and discharging efficiency of the energy storage unit, Represents the remaining discharge time of the energy storage unit. Represents the maximum discharge power of the energy storage unit, Represents the power required by the current load; Based on the evaluation value of the energy storage unit's discharge capacity, combined with the energy storage unit's discharge power and load demand power, the energy storage unit's discharge priority is calculated using the formula: ; in, represents the current available capacity of the energy storage unit, Represents the charging and discharging efficiency of the energy storage unit, Represents the remaining discharge time of the energy storage unit. Represents the maximum discharge power of the energy storage unit, Represents the power required by the current load. Assume that the parameters of a certain energy storage unit are as follows: , , , , , the discharge priority is calculated as follows: ; If there are multiple energy storage units in the system, the discharge priority of all energy storage units is calculated and sorted from large to small according to the value to obtain the discharge priority of the energy storage unit, as shown in Table 2. For the denominator in the discharge priority calculation, the reference value is set to 1 to prevent the influence of zero value. Part of it is used to measure the load adaptability of the energy storage unit, and the smaller This means that the energy storage unit is more suitable for the current load. Therefore, the value setting range is usually 0 to 10. The specific value is adjusted according to the matching degree between the load demand and the rated output power of the energy storage unit. For example, if the maximum discharge power of the energy storage unit is 35kW and the current load demand is 33kW, the difference is , in line with the principle of priority scheduling.

[0033] Table 2 Energy storage unit discharge priority As shown in Table 2, the discharge priority of energy storage unit A is the highest, so the discharge of A is given priority in the scheduling strategy.

[0034] S413: Based on the discharge priority of the energy storage unit, the energy storage power release order is set from high to low priority, the power scheduling of the energy storage unit under each load state is matched, and the peak shaving discharge order of the energy storage cluster is obtained.

[0035] Based on the discharge priority of the energy storage unit, the energy storage power release order is set from high to low priority, matching the energy storage unit power scheduling under each load state. According to the discharge priority order, the optimal energy storage unit is determined for discharge, and the maximum discharge power constraint is set. For example, if the maximum discharge power of energy storage unit A is 30kW and the maximum discharge power of energy storage unit B is 35kW, the energy storage scheduling under the actual load demand is calculated. If the current load demand power is 50kW, energy storage unit A will release 30kW first, and the remaining 20kW will be provided by energy storage unit B. The energy storage power distribution is calculated as follows: ; ; The calculated discharge power distribution is as follows: energy storage unit A discharges 30kW, energy storage unit B discharges 20kW, and energy storage unit C does not participate in the discharge.

[0036] When the load demand changes, the discharge strategy of the energy storage unit is dynamically adjusted. For example, if the load demand drops to 40kW, energy storage unit A discharges 30kW, energy storage unit B discharges 10kW, and energy storage unit C still does not participate, as shown in Table 3.

[0037] Table 3 Peak shaving discharge sequence of energy storage cluster As shown in Table 3, as the load demand changes, the discharge power of the energy storage unit is dynamically matched according to priority to obtain the peak shaving discharge sequence of the energy storage cluster.

[0038] See also Figure 6 , the specific steps for obtaining energy storage dispatch adjustment parameters are as follows: S511: Call the peak shaving discharge sequence of the energy storage cluster, calculate the required energy storage discharge amount during the peak load period, and screen the energy storage units that can be used for scheduling based on the current discharge power, health status and temperature change rate of each energy storage unit to obtain the dispatchable capacity evaluation value of the energy storage unit; After calling the peak-shaving discharge sequence of the energy storage cluster, calculate the energy storage discharge required during the peak load period. First, determine the peak load period, which generally refers to the time interval when the load demand exceeds the set load benchmark value, such as the peak load benchmark value set by a system. The value is 100kW. The value is set based on the 80% percentile of the historical load data in the area, that is, in the load data of the load system in the past month, the load did not exceed this value for 80% of the time. Therefore, this value is used as the peak load benchmark. If the actual load demand in a certain period of time is 150kW, the energy storage discharge amount that needs to be compensated is calculated as follows: ; After calculating the energy storage discharge demand during the peak load period, the current discharge power, health status and temperature change rate of each energy storage unit are combined for screening. The discharge power refers to the actual discharge power of the energy storage unit at the current moment. For example, the discharge power of a certain energy storage unit is 30kW. The health status indicates the remaining life of the energy storage unit or the degree of battery attenuation. This parameter uses the health status factor , this factor is between 0 and 1, the higher the value, the better the health status, and the setting standards are as follows: 0.9-1.0: less than 500 charge and discharge cycles, the internal battery impedance changes ≤2%; 0.7-0.9: 500-1000 charge and discharge cycles, the internal battery impedance changes 2%-5%; 0.5-0.7: 1000-2000 charge and discharge cycles, the internal battery impedance changes 5%-10%; 0.3-0.5: 2000-3000 charge and discharge cycles, the internal battery impedance changes 10%-15%; <0.3: more than 3000 times, the internal battery impedance changes >15%. For example, if a storage unit has run 600 charge and discharge cycles and the internal battery impedance changes by 3%, then its health status factor is .

[0039] The temperature change rate is defined as the rate at which the ambient temperature affects the performance of the energy storage unit. This value is derived from the temperature characteristic data of the energy storage battery. For example, the discharge efficiency of the energy storage battery is highest at 25°C, but when the temperature rises by 1°C, the discharge efficiency decreases by 0.1%. Therefore, the temperature change rate affects the coefficient The settings are as follows: When: temperature rise rate ≤ 0.5°C / min; When: temperature rise rate 0.5-1.0°C / min; When: Temperature rise rate ≥ 1.0°C / min. Assuming the current temperature change rate is 0.6°C / min, then , and finally calculate the dispatchable capacity evaluation value of the energy storage unit.

[0040] S512: Based on the dispatchable capacity evaluation value of the energy storage unit, combined with the health status, temperature change rate and current discharge power of the energy storage unit, the formula is used: ; Calculate and obtain the energy storage unit discharge time distribution value; in, Represents the discharge time distribution value of the energy storage unit, represents the current available capacity of the energy storage unit, Represents the preset minimum safety capacity of the energy storage unit. Represents the charging and discharging efficiency of the energy storage unit, Represents the health status influencing factor of the energy storage unit, represents the discharge power of the energy storage unit, Represents the temperature change rate influence coefficient of the energy storage unit; Based on the dispatchable capacity evaluation value of the energy storage unit, combined with the health status, temperature change rate and current discharge power of the energy storage unit, the discharge time allocation value of the energy storage unit is calculated using the formula: ; Among them, the minimum safe capacity It is a key parameter to ensure the long-term life of the energy storage unit. A value lower than this value may accelerate the performance degradation of the energy storage device. This value is usually set according to the battery depth of discharge (DOD) limit. The recommended minimum safe power of common lithium battery energy storage units is 25% of the total capacity, that is, ; Assuming the maximum energy storage capacity of a certain energy storage unit is 80kWh, the minimum safety capacity is calculated as follows: ; For charge and discharge efficiency , its value is affected by the battery type, for example: lithium iron phosphate battery: (High efficiency); ternary lithium battery: (General); Lead-acid batteries: (Low efficiency). Assuming that the energy storage unit is a lithium iron phosphate battery, then , bring into calculation: ; The discharge time distribution values ​​of all energy storage units are calculated and sorted from large to small according to the values ​​to obtain the discharge time distribution of the energy storage units, as shown in Table 4.

[0041] S513: Based on the energy storage unit discharge time allocation value, adjust the energy storage unit discharge time, optimize the energy storage unit discharge time allocation, and establish energy storage scheduling adjustment parameters.

[0042] Based on the energy storage unit discharge time allocation value, adjust the energy storage unit discharge time, optimize the energy storage unit discharge time allocation, first sort the energy storage units according to the discharge time allocation value, give priority to the energy storage units with longer discharge time for discharge scheduling, and ensure that the minimum safety capacity of the energy storage unit is not exceeded during the allocation process, that is, the remaining energy of the energy storage unit after discharge shall not be lower than the set threshold , if the current energy of the energy storage unit is 50kWh, the discharge time allocation value is 1.2h, if it is discharged at a power of 30kW, the remaining energy after discharge is calculated as follows: ; Since the remaining energy is lower than the minimum safety capacity Therefore, the discharge power must be adjusted or the discharge time must be reduced. For example, if the discharge power is reduced to 25kW, the new discharge time allocation is calculated as follows: ; Adjust the energy storage scheduling strategy as shown in Table 4.

[0043] Table 4 Energy storage dispatch adjustment parameters As shown in Table 4, the discharge time of the energy storage unit C is adjusted to ensure that the minimum safety capacity of the energy storage unit is not exceeded, and finally the energy storage scheduling adjustment parameters are established to meet the system's scheduling requirements for peak shaving and valley filling.

[0044] A system for improving the power supply potential of emerging loads, which is used to execute the above-mentioned method for improving the power supply potential of emerging loads, and the system includes: The load characteristic identification module obtains the power demand data of the user in multiple time periods, calculates the load change rate in each time period, constructs a load behavior pattern characteristic set, and obtains load behavior pattern characteristic data; The power demand comparison module calculates the similarity score between the current data and the known load pattern based on the load behavior pattern characteristic data, then calls the load change rate of the adjacent time period, calculates the adjustment amount of the current power demand, and obtains the real-time power demand comparison mapping information; The power supply time adjustment module calls the real-time power demand comparison mapping information, screens the time period when the current load demand change range exceeds the load fluctuation allowable range, and corrects the load acceptable power supply adjustment time range in combination with the change trend of the load operation status to obtain the load dynamic response window; The discharge sequence optimization module calls the load dynamic response window to obtain the current available capacity of the energy storage device, calculates the discharge priority of the energy storage unit based on the current state of the energy storage unit, and generates the peak shaving discharge sequence of the energy storage cluster; The discharge parameter adjustment module calls the peak-shaving discharge sequence of the energy storage cluster, calculates the energy storage discharge required during the peak load period, adjusts the discharge duration of the energy storage unit, and obtains the energy storage scheduling adjustment parameters.

[0045] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for improving the power supply potential of emerging loads, characterized in that: The following steps are involved: S1: Obtain the power demand data of the user in multiple time periods, calculate the load change rate in each time period, construct a load behavior pattern feature set, and obtain load behavior pattern feature data; S2: Based on the load behavior pattern characteristic data, the similarity score between the current data and the known load pattern is calculated, and the load change rate of the adjacent time period is called to calculate the adjustment amount of the current power demand, and obtain the real-time power demand comparison mapping information; S3: calling the real-time power demand comparison mapping information, screening the time period when the current load demand change amplitude exceeds the load fluctuation allowable range, and correcting the load acceptable power supply adjustment time range in combination with the change trend of the load operation state to obtain the load dynamic response window; S4: calling the load dynamic response window, obtaining the current available capacity of the energy storage device, calculating the discharge priority of the energy storage unit according to the current state of the energy storage unit, and generating the peak shaving discharge sequence of the energy storage cluster; S5: calling the peak shaving discharge sequence of the energy storage cluster, calculating the energy storage discharge amount required during the peak load period, adjusting the discharge duration of the energy storage unit, and obtaining the energy storage scheduling adjustment parameters.

2. The method for improving the power supply potential of emerging loads according to claim 1, characterized in that: The load behavior pattern characteristic parameters include daily load fluctuation amplitude, weekly load balancing coefficient, monthly load growth ratio, load change rate, and power demand stability; the real-time power demand comparison mapping information specifically includes power demand mapping value, load matching threshold, similarity score, interpolation calculation optimization value, and adjacent load pattern parameters; the load dynamic response window parameters include load starting power, stable operating power, load allowable fluctuation range, power supply adjustment window duration, and load fluctuation rate; the load priority parameters specifically include load category, grid load margin, equipment power demand, response priority setting value, and load adjustment priority. The energy storage cluster discharge optimization strategy includes the remaining available capacity of the energy storage system, the current grid load level, the energy storage unit discharge power, the discharge strategy setting value, and the discharge sequence adjustment value.

3. The method for improving the power supply potential of emerging loads according to claim 1, characterized in that: The steps for acquiring the load behavior pattern characteristic data are specifically as follows: S111: obtaining power demand data of the user in multiple time periods, extracting the load fluctuation amplitude, load balancing coefficient and load growth ratio in each time period, calculating the load change rate in each time period, and calculating the standard deviation of the load change in multiple time periods according to the load change rate to obtain the load change dispersion; S112: Based on the load variation dispersion, determine the stability of the power demand data, classify the stability into multiple levels, and combine the load variation rate of each level to adopt the formula: ； The stability deviation value is obtained by calculation, and the power demand stability deviation coefficient is obtained; in, represents the power demand stability deviation coefficient, Representative The load change rate in a time period, represents the average load change rate over a time period, Represents the total number of time periods; S113: Based on the power demand stability deviation coefficient, the change trends of the load fluctuation amplitude, the load balancing coefficient and the load growth ratio are extracted, a load behavior pattern feature set is constructed, and load behavior pattern feature data is obtained.

4. The method for improving the power supply potential of emerging loads according to claim 1, characterized in that: The steps for obtaining the real-time power demand comparison mapping information are specifically as follows: S211: Based on the load behavior pattern characteristic data, the actual load data of the user in the current time window is obtained, using the formula: ； The matching degree between the actual load data and the known load pattern is calculated to obtain the load similarity score; in, represents the load similarity score, Represents the current Actual data of load characteristics, Represents a known load pattern The corresponding data of the load characteristics, represents the total number of load characteristics; S212: Based on the load similarity score, determine whether the score is lower than the load matching threshold. If it is lower than the threshold, call the load change rate of the adjacent time period, calculate the average change rate of each time period, and obtain the adjustment amount of the current power demand to obtain the real-time power demand adjustment value; S213: Based on the real-time power demand adjustment value, calculate the offset degree of the current power demand mapped to the load behavior pattern, compare it with the known load pattern, calculate the adjusted load change trend, and obtain the real-time power demand comparison mapping information.

5. The method for improving the power supply potential of emerging loads according to claim 1, characterized in that: The steps for obtaining the load dynamic response window are specifically as follows: S311: Obtain the real-time power demand comparison mapping information, extract the current load state parameters, obtain the user-side load starting power and stable operating power, calculate the load allowable fluctuation range, adjust the load fluctuation upper and lower limits according to the power demand characteristics of the load equipment, and obtain the load power fluctuation parameters; S312: Call the load power fluctuation parameter to obtain the load operation time, load fluctuation amplitude, and user-acceptable load adjustment time, using the formula: ； Calculate and obtain the power supply adjustment time range, and generate power supply adjustment window parameters; in, Represents the duration of the power supply adjustment window. Represents stable operating power, Represents the load starting power, Represents the load adjustment response rate, represents the critical value of user load demand, Representative Adjustable power factor of each load unit, Represents the total number of load units used for calculation; S313: calling the power supply adjustment window parameters, obtaining the load fluctuation rate, calculating the dynamic correction ratio of the current power supply adjustment window, adjusting the upper limit and the lower limit of the power supply adjustment window, and obtaining the load dynamic response window.

6. The method for improving the power supply potential of emerging loads according to claim 1, characterized in that: The specific steps for obtaining the peak shaving discharge sequence of the energy storage cluster are: S411: calling the load dynamic response window, obtaining the current available capacity of the energy storage device, calculating the load change rate per unit time, screening the discharge power, remaining discharge time, and charge and discharge efficiency of each energy storage unit, calculating and obtaining the state parameters of each energy storage unit, and obtaining the discharge capacity evaluation value of the energy storage unit; S412: Based on the evaluation value of the discharge capacity of the energy storage unit, combined with the discharge power of the energy storage unit and the load demand power, the formula is adopted: ； Obtaining the priority ranking value of each energy storage unit by calculation, and obtaining the discharge priority of the energy storage unit; in, Represents the discharge priority of the energy storage unit, represents the current available capacity of the energy storage unit, Represents the charging and discharging efficiency of the energy storage unit, Represents the remaining discharge time of the energy storage unit. Represents the maximum discharge power of the energy storage unit, Represents the power required by the current load; S413: Based on the energy storage unit discharge priority, the energy storage power release sequence is set from high to low priority, the energy storage unit power scheduling under each load state is matched, and the peak shaving discharge sequence of the energy storage cluster is obtained.

7. The method for improving the power supply potential of emerging loads according to claim 1, characterized in that: The steps for obtaining the energy storage scheduling adjustment parameters are specifically as follows: S511: calling the peak shaving discharge sequence of the energy storage cluster, calculating the required energy storage discharge amount during the peak load period, and screening the energy storage units that can be used for scheduling in combination with the current discharge power, health status and temperature change rate of each energy storage unit to obtain the dispatchable capacity evaluation value of the energy storage unit; S512: Based on the dispatchable capacity evaluation value of the energy storage unit, combined with the health status, temperature change rate and current discharge power of the energy storage unit, the formula is used: ； Calculate and obtain the energy storage unit discharge time distribution value; in, Represents the discharge time distribution value of the energy storage unit, represents the current available capacity of the energy storage unit, Represents the preset minimum safety capacity of the energy storage unit. Represents the charging and discharging efficiency of the energy storage unit, Represents the health status influencing factor of the energy storage unit, represents the discharge power of the energy storage unit, Represents the temperature change rate influence coefficient of the energy storage unit; S513: Based on the energy storage unit discharge time allocation value, adjust the discharge time of the energy storage unit, optimize the discharge time allocation of the energy storage unit, and establish energy storage scheduling adjustment parameters.

8. A system for improving the power supply potential of emerging loads, characterized in that: According to the method for improving the power supply potential of emerging loads according to any one of claims 1 to 7, the system comprises: The load characteristic identification module obtains the power demand data of the user in multiple time periods, calculates the load change rate in each time period, constructs a load behavior pattern characteristic set, and obtains load behavior pattern characteristic data; The power demand comparison module calculates the similarity score between the current data and the known load pattern based on the load behavior pattern characteristic data, then calls the load change rate of the adjacent time period, calculates the adjustment amount of the current power demand, and obtains the real-time power demand comparison mapping information; The power supply time adjustment module calls the real-time power demand comparison mapping information, corrects the acceptable power supply adjustment time range of the load, and obtains the load dynamic response window; The discharge sequence optimization module calls the load dynamic response window, obtains the current available capacity of the energy storage device, calculates the discharge priority of the energy storage unit, and generates the peak shaving discharge sequence of the energy storage cluster; The discharge parameter adjustment module calls the peak shaving discharge sequence of the energy storage cluster, calculates the energy storage discharge amount required during the peak load period, adjusts the discharge duration of the energy storage unit, and obtains the energy storage scheduling adjustment parameters.

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