Edge intelligent calculation energizing type peak-valley energy storage charging system

Abstract

The invention relates to the technical field of intelligent charging, in particular to an edge intelligent calculation enabling type peak-valley energy storage charging system which comprises a power grid frequency judgment module, an energy storage dispatching output module, a load trend recognition module, a multi-station matching sorting module and a load dynamic distribution module. According to the invention, through the continuous data analysis of the power grid frequency, the energy storage unit power and residual energy, and the load current, the system can accurately identify the linkage relationship of various parameters during the frequency abnormal response period, refine the condition judgment of peak-valley energy storage triggering and scheduling, optimize the energy storage output decision path, and improve the reliability of the system. Through dynamic screening of load trends and changing stations, the real-time performance and flexibility of energy allocation among multiple stations are enhanced, the adaptive capacity of the system to complex supply and demand changes is further improved in station matching and priority ranking, energy storage resources and loads are dynamically allocated, the electric energy utilization rate and the cooperative regulation and control level of the energy storage system are effectively improved, and the energy utilization rate and the cooperative regulation and control level of the energy storage system are improved. And intelligent upgrading of energy management is promoted.

Description

Technical Field

[0001] The present invention relates to the field of intelligent charging technology, and in particular to an edge intelligent computing-enabled peak-valley energy storage charging system. Background Art

[0002] The smart charging sector involves the efficient supply and dynamic distribution of electric energy, including power system optimization, including electric vehicle charging, energy storage management, and energy scheduling. This encompasses the management and control of charging power, charging time, energy utilization efficiency, and the sequence of charging and discharging. Through the coordinated cooperation of power conversion and energy storage equipment, the utilization rate of electric energy and the operational efficiency of charging infrastructure are improved. Among them, the traditional edge-based intelligent computing-enabled peak-valley energy storage charging system refers to a system that uses edge computing technology to collect and conduct real-time data analysis on key links such as load forecasting, energy scheduling, and energy storage control at local charging stations. Combined with peak-valley electricity price management strategies, it uses energy storage equipment to charge and store energy during low-load periods on the grid and discharge and supply energy during peak periods, thereby alleviating grid pressure and optimizing energy distribution at charging stations.

[0003] In actual scenarios, existing technologies use fixed parameters as the scheduling basis, lacking a comprehensive assessment of the linkage changes between real-time grid anomalies and energy storage unit status. When encountering large-scale load fluctuations or changes in energy demand at multiple sites, the response strategy lags and cannot dynamically adjust the energy storage trigger and output conditions according to the parameters. Load trends and anomalies are not identified in a timely manner, and the energy distribution path is rigid, resulting in insufficient energy storage response and load balancing capabilities. The flexibility and efficiency of system operation are limited, and it is difficult to achieve multi-dimensional energy adaptive distribution and real-time resource optimization in the face of complex working conditions. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the existing technology and propose an edge intelligent computing enabled peak-valley energy storage and charging system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an edge intelligent computing-enabled peak-valley energy storage charging system, the system comprising:

[0006] The grid frequency identification module analyzes the output power, remaining capacity, and load current changes of the energy storage unit based on the grid frequency signals synchronously collected by the edge intelligent computing nodes at each site. It compares the trends of frequency and energy storage parameters, determines the linkage characteristics, and monitors the abnormal recovery process to obtain the abnormal frequency linkage characteristics.

[0007] The energy storage scheduling output module screens the output power, releasable power and number of the energy storage units based on the abnormal frequency linkage characteristics, determines whether the releasable power meets the peak and valley energy supply conditions, sorts the numbers and issues output instructions, and calculates the power and current of the energy supply units to obtain the scheduled energy storage output;

[0008] The load trend identification module compares the power load of each site with the load change trend over a period of time based on the scheduled energy storage output, screens sites with abnormal load change trends, sorts the load detection data of the abnormal sites, analyzes the difference in load changes between the top two sites in the sorting, and obtains the site load change range;

[0009] The multi-station matching sorting module calculates the remaining energy storage and power load of the site based on the site load variation range, compares the matching relationship, sorts the sites by priority according to the matching degree, dynamically adjusts the sorting structure, and obtains a priority site matching set.

[0010] The present invention has the following improvements: the abnormal frequency linkage characteristics include disturbance type category, response peak amplitude, and linkage duration period; the dispatched energy storage output includes energy storage output amplitude, dispatch period identifier, and dispatch unit set; the site load change interval includes the maximum change amount of the interval, the change rate level, and the associated site serial number; the priority site matching set includes a sorting weight parameter, a site priority identifier, and a matching sequence number.

[0011] The present invention is improved in that the power grid frequency determination module includes:

[0012] The frequency trend extraction submodule analyzes the time series data of energy storage unit output power, remaining capacity, and load current collected synchronously at each site based on the grid frequency signals collected synchronously by the edge computing nodes at each site. By calculating the amplitude and direction of frequency changes at each time node and comparing the relationship between frequency trends and energy storage output during the difference phases, the difference in the power-frequency linkage trend is determined.

[0013] The energy storage status linkage analysis submodule analyzes the changes in energy storage output power, remaining capacity, and load current at the same site over multiple cycles based on the differences in the frequency-power linkage trends. It calculates the directional consistency of each indicator and determines the energy storage condition linkage interval range by screening the periodic data that meets the conditions.

[0014] The frequency anomaly evolution detection submodule analyzes the frequency data sequence based on the amplitude of the energy storage condition linkage interval, determines the continuous changes in the energy storage output power and load current between the start and end points of the abnormal phase, calculates the response of the energy storage power and load current to the frequency disturbance, and obtains the abnormal frequency linkage characteristics by combining the disturbance type and period characteristics.

[0015] The present invention is improved in that the energy storage scheduling output module includes:

[0016] Based on the abnormal frequency linkage characteristics, the energy storage power screening submodule analyzes the output power, number, and available power of the energy storage units synchronously collected by each edge intelligent computing node, compares the current frequency change trend with the number power response characteristics, determines which energy storage units meet the response scheduling conditions, and screens the numbers participating in the scheduling to obtain the number power linkage set;

[0017] The releasable power determination submodule calculates the releasable power corresponding to each energy storage unit based on the numbered power linkage set, analyzes the release capacity required in the current peak and valley operation phase, screens energy storage numbers that meet the energy supply conditions, optimizes the energy storage unit classification, and obtains the dispatchable energy storage number set;

[0018] The dispatch instruction generation submodule compares the output power, releasable power and frequency response of each number based on the dispatchable energy storage number set, optimizes the number priority, calculates the unified output amplitude of the energy storage, adjusts the output mode of each number, issues dispatch instructions and monitors the response of each energy storage unit to obtain the dispatched energy storage output.

[0019] The present invention is improved in that the load trend identification module includes:

[0020] The load trend acquisition submodule analyzes the site's power load monitoring data based on the dispatched energy storage output, compares the changes in power load curves over consecutive time periods, and screens the load trend segments with key fluctuations within the cycle to obtain a key load fluctuation curve;

[0021] The abnormal trend screening submodule determines the change of trend curves of each site based on the key load fluctuation curve, compares and finds sites with key abnormal change rates, optimizes and sorts the trend curves of the top two sites, and obtains the site abnormal amplitude sequence;

[0022] The variation interval calculation submodule compares the trend amplitudes of the first two sites in the same period according to the site abnormal amplitude sequence, analyzes the difference in load changes between the corresponding numbers of the two sites, determines the boundary of the variation interval, and obtains the site load variation interval.

[0023] The present invention is improved in that the multi-station matching and sorting module includes:

[0024] The energy storage load comparison submodule compares the current remaining energy storage at each site with the electricity load during the same period based on the site load variation interval, calculates the difference between the energy storage and electricity load during the same period, and then synchronously normalizes the maximum load variation amplitude and the energy storage difference to obtain the energy storage load adaptation difference amplitude;

[0025] The matching degree calculation submodule calculates the load change rate and energy storage release rate of the site during the same period based on the energy storage load adaptation difference, compares the load distribution differences between the sites, and obtains the matching degree level between the sites;

[0026] The priority sorting submodule analyzes the matching degree and site number of each screening site based on the matching degree level between the sites, adjusts the priority one by one according to the matching degree level, optimizes the arrangement order of the matching sites, and obtains a priority site matching set.

[0027] The present invention is improved in that the system further comprises:

[0028] The load dynamic distribution module analyzes the energy storage output and load distribution of the target site based on the priority site matching set, determines that the priority site is the regulation object, adjusts the energy storage output mode, compares the load distribution changes before and after the adjustment, and obtains the load distribution change amplitude;

[0029] The load distribution change range includes the adjusted load value, the distribution target site, and the distribution batch number.

[0030] The present invention is improved in that the load dynamic distribution module includes:

[0031] The priority site identification submodule analyzes the ranking order of each site and the current energy storage output and load distribution based on the priority site matching set, determines the site with the best priority and whose energy storage and load characteristics meet the regulation conditions, and compares the priorities and controllability of multiple sites to obtain the target site number for regulation;

[0032] The energy storage mode adjustment submodule analyzes the current energy storage output mode and energy storage response characteristics of the control target site according to the control target site sequence number, optimizes the adjustment mode to adapt to the real-time load distribution, and compares the historical energy storage response with the current state change to determine the optimal energy storage output mode and obtain the energy storage output type;

[0033] The load amplitude calculation submodule analyzes the load distribution data before and after the adjustment based on the energy storage output type, calculates the load change range caused by the energy storage mode adjustment, compares the load distribution shift caused by the adjustment, screens and determines the degree of load redistribution, and obtains the load distribution change amplitude.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are:

[0035] In the present invention, through continuous data analysis of grid frequency, energy storage unit power and surplus energy, and load current, the system can accurately identify the linkage relationship between various parameters during frequency anomaly response, refine the conditional judgment of peak and valley energy storage triggering and scheduling, optimize the energy storage output decision path, and enhance the real-time and flexibility of energy allocation between multiple sites through dynamic screening of load trends and changing sites. The system's adaptability to complex supply and demand changes is further improved in site matching and priority sorting, and energy storage resources and loads are dynamically allocated. This effectively improves the power utilization rate and the coordinated regulation level of the energy storage system, promotes the intelligent upgrade of energy management, and promotes adaptive energy balance under different operating scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a system flow chart of the present invention;

[0037] Figure 2 This is a flow chart of the power grid frequency discrimination module in the present invention;

[0038] Figure 3 This is a flow chart of the energy storage scheduling output module in the present invention;

[0039] Figure 4 This is a flow chart of the load trend identification module in the present invention;

[0040] Figure 5 This is a flow chart of the multi-station matching and sorting module in the present invention;

[0041] Figure 6 This is a flow chart of the load dynamic allocation module in the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to 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.

[0043] 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", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0044] Example

[0045] See also Figure 1 , the present invention provides a technical solution: an edge intelligent computing enabled peak-valley energy storage charging system includes:

[0046] The grid frequency identification module analyzes the changes in the output power, remaining capacity, and load current of the energy storage unit based on the grid frequency signals synchronously collected by the edge computing nodes at each site. It compares the changing trends between the current grid frequency and the on-site energy storage parameters, determines whether the linkage characteristics between the parameters meet the peak and valley energy storage startup standards, and monitors the continuous changes in the differences between the parameters during the occurrence and recovery phases of frequency anomalies to obtain the abnormal frequency linkage characteristics.

[0047] The energy storage scheduling output module, based on the abnormal frequency linkage characteristics, screens the output power, releasable power, and number of the energy storage units associated with the edge intelligent computing node, determines whether the current releasable power of the energy storage unit meets the peak and valley energy supply conditions, sorts the filtered energy storage unit numbers, issues output instructions, and then counts the output power and current data of the energy supply unit to obtain the scheduled energy storage output;

[0048] The load trend identification module compares the power load of each site with the load change trend over a period of time based on the scheduled energy storage output. It then screens sites with abnormal load change trends, ranks the load detection data of abnormal sites, and analyzes the difference in load changes between the top two sites to determine the site load change range.

[0049] The multi-station matching and sorting module calculates the remaining energy storage and electricity load data of the associated stations based on the station load variation range, compares the matching relationship between the remaining energy storage and the load, prioritizes each station according to the matching degree, and dynamically adjusts the sorting structure to obtain a priority station matching set;

[0050] The dynamic load distribution module analyzes the real-time energy storage output and load distribution of the target site based on the priority site matching set, determines the site with the best priority as the control object, adjusts the energy storage output method of the site, and compares the changes in the site load distribution before and after the adjustment to obtain the magnitude of the load distribution change.

[0051] The abnormal frequency linkage characteristics include the disturbance type category, response peak amplitude, and linkage duration period; the dispatched energy storage output includes the energy storage output amplitude, the dispatch period identifier, and the dispatch unit set; the site load change interval includes the maximum interval change, the change rate level, and the associated site sequence number; the priority site matching set includes the sorting weight parameter, the site priority identifier, and the matching sequence number; the load distribution change amplitude includes the adjusted load value, the distribution target site, and the distribution batch number.

[0052] In the grid frequency identification module, edge computing nodes refer to edge computing terminal devices deployed at charging stations, energy storage stations, etc. for local high-speed processing, analysis, and decision-making, which can collect, calculate, and issue control instructions in real time; grid frequency signals refer to the actual grid operating frequency detected by the grid acquisition equipment in the power system, which is used to judge the current stability of the grid; energy storage units refer to battery system modules or energy storage devices such as supercapacitors with independent control and monitoring capabilities, and each unit can participate in energy storage management and scheduling independently; change trends refer to the direction and magnitude of changes in a certain parameter (such as frequency, power, load, etc.) over a period of time, such as increase, decrease, or fluctuation; linkage features refer to multiple parameters ( The rules of mutual influence between various parameters (such as frequency, energy storage power, and load current), for example, the synergistic phenomenon of increased output power of energy storage units when the grid frequency drops; peak and valley energy storage startup standards refer to the criteria and benchmarks used to determine whether the energy storage unit enables charging and discharging operations during peak or valley periods, usually based on the combined status of a set of real-time parameters (such as frequency fluctuations, power margin, etc.); frequency anomaly refers to the phenomenon that the grid operating frequency exceeds the safe operating range, indicating the existence of supply and demand imbalance, shock or failure; the difference between various parameters in the recovery phase refers to the real-time changes and mutual response characteristics between various monitoring parameters (such as frequency, energy storage output, and load current) in the process of eliminating frequency anomalies and returning to the normal range.

[0053] In the energy storage scheduling output module, the releasable power refers to the remaining power that the energy storage unit can currently use to output, meet charging needs, or support the power grid; the number refers to the unique identification code of the energy storage unit, which is used by the scheduling system to identify, sort, and operate the specific unit; the peak-valley energy supply condition refers to the rules and standards set according to the system strategy to determine whether the energy storage unit can (or needs to) output or charge during peak hours (high load) or valley hours (low load); the output instruction refers to the control command issued by the system to the energy storage unit, requiring it to charge or discharge according to the set power within a specific time.

[0054] In the load trend identification module, the power load of each site refers to the total amount of electricity currently consumed by each charging station, energy storage station or local microgrid, reflecting the current power demand status of the site; the load change trend refers to the growth, decline or fluctuation trajectory of the power load within a specified time period, which is used to reflect the site demand dynamics; abnormal sites refer to sites where the power load change trend is found to deviate from the normal range, has sudden changes, or fluctuates violently through analysis; load detection data refers to real-time and historical data information about power load collected by sensors and collection equipment; the difference in load changes refers to the comparison results of the power load change amplitude between different sites or different time periods, which is used to identify sites with large changes.

[0055] In the multi-station matching and sorting module, associated sites refer to all charging stations, energy storage stations, or microgrid nodes in the current system that need to participate in energy storage scheduling and load balancing; the matching relationship between remaining energy storage and load refers to the mutual adaptation between the remaining dispatchable energy storage of each site and the current power load, reflecting the ability of the site to carry new loads or release energy storage; the degree of matching refers to the adaptability of the remaining energy storage and load demand obtained through calculation, which is usually measured by ratios, correlations, and other standards; dynamic adjustment of the sorting structure refers to the operation process of automatically re-sorting the priority and resource allocation order of each site according to real-time changes in the system operating status.

[0056] In the load dynamic distribution module, real-time energy storage output refers to the current charging and discharging power and continuous output status of the energy storage units at each site; load distribution refers to the current distribution of power loads among multiple sites, reflecting the load balancing level of the overall system; the control object refers to the specific site selected by the system as the target of this round of scheduling and output adjustment; energy storage output mode: refers to the working status of the energy storage unit, including specific operating modes such as charging, discharging, and standby; load distribution change refers to the changes in the load distribution pattern and values ​​between sites before and after system adjustment, which is used to analyze the scheduling effect and load balancing level.

[0057] See also Figure 2 , the grid frequency identification module includes:

[0058] The frequency trend extraction submodule analyzes the time series data of energy storage unit output power, remaining capacity, and load current collected synchronously at each site based on the grid frequency signals collected synchronously by the edge computing nodes at each site. By calculating the amplitude and direction of frequency changes at each time node and comparing the relationship between frequency trends and energy storage output during the difference phases, the difference in the power-frequency linkage trend is determined.

[0059] The frequency values, energy storage unit output power values, remaining power values ​​and load current values ​​collected at each site are sampled at a sampling frequency of one time point per second to form a corresponding time series data sequence. Each parameter sequence needs to be precisely aligned according to the timestamp to ensure data consistency and parallelism. In actual sampling, for example, if an analysis cycle is 300 seconds, the length of each data set is 300. Then, in the frequency sequence of each site, the absolute value of the frequency difference between two adjacent time points is taken and marked as the frequency change amplitude. If the current value is greater than the value at the previous time point, the direction of the period is rising, otherwise it is falling. If the absolute value of the difference is lower than the set fluctuation identification reference value, such as 0.05Hz, the paragraph is marked as a stable trend. Then, under the division of stable frequency trend, the change amplitude of energy storage output power, remaining power and load current in the corresponding period is extracted, and expressed as the difference between the current value and the previous value, to form a time period parameter change sequence consistent with the frequency trend. The energy storage output power and frequency changes in each segment are analyzed. The power and frequency trends within each segment are compared for synchronization. When the two change in the same direction, positive linkage is observed; changes in opposite directions are negative linkage; and no significant change is considered weak linkage. The degree of linkage is assessed by counting the number of time points within each segment at which the frequency changes are consistent with the other parameters. For example, if 240 of 300 time points have consistent directions for the three parameters, the consistency ratio is 80%, indicating strong linkage. If only 120 time points have consistent directions, the consistency ratio is 40%, indicating weak linkage. This method allows for five classification levels: exceeding 80% for extremely strong linkage, 65%-80% for strong linkage, 50%-65% for moderate linkage, 35%-50% for weak linkage, and below 35% for extremely weak linkage. Finally, the linkage status of each site in different time periods is classified by level, and the differences in the power-frequency linkage trends of different sites in different frequency trend stages are output.

[0060] The energy storage status linkage analysis submodule analyzes the changes in energy storage output power, remaining capacity, and load current at the same site over multiple cycles based on differences in power-frequency linkage trends. It calculates the directional consistency of each indicator and, by screening eligible periodic data, determines the energy storage condition linkage range.

[0061] In each linkage trend cycle, the output power, remaining power and load current values ​​of the energy storage unit of the current site in this time period are extracted to form a three-column data list with time as the horizontal axis. Then, for the three parameters of each second, the increase and decrease directions of the adjacent time points are calculated one by one. By judging the size relationship between the current value and the value of the previous moment, if the three increase or decrease at the same time, they are considered to have the same direction, otherwise they are inconsistent in direction. The number of time points with consistent directions in the entire cycle is counted, and the value is divided by the total duration of the cycle to obtain the direction consistency ratio. When the ratio exceeds 65%, it indicates that the changes of the three parameters in the cycle show a synchronous trend. If it is less than 65%, the cycle is eliminated and no further processing is performed. Subsequently, the difference between the maximum and minimum values ​​of each parameter in the direction-consistent cycle is calculated, which respectively represents the fluctuation amplitude of the parameter. The fluctuation amplitudes of the three parameters are multiplied by preset weighting factors, where energy storage power has the highest weight, followed by remaining power, and then load current. This forms a weighted sum value, which is used to reflect the strength of the linkage amplitude within the cycle. The threshold is set to 1.5. If the weighted sum is greater than this value, the cycle is judged to be a period of strong linkage in the energy storage state. If it is lower than this value, the linkage amplitude is considered to be insignificant and is not included in the analysis scope. For example, within a certain site cycle, the maximum power variation is 1.5 kilowatts, the power variation is 3%, and the load current variation is 1.2 amps. After substituting the weights, the weighted linkage amplitude is calculated to be 1.89, which is higher than the set threshold. Therefore, this cycle is retained as a valid analysis segment. The time periods that meet the direction consistency and linkage amplitude conditions for all sites in multiple cycles are integrated, and the energy storage condition linkage interval amplitude corresponding to the time period is output.

[0062] The frequency anomaly evolution detection submodule analyzes the frequency data sequence based on the energy storage condition linkage interval amplitude, and determines the continuous changes of energy storage output power and load current during the start and end points of the abnormal phase using the formula:

[0063]

[0064] Calculate the response of energy storage power and load current to frequency disturbance, and combine the disturbance type and period characteristics to obtain the abnormal frequency linkage feature GS, where Q i represents the energy storage output power at the i-th moment, Q i-1 Indicates the energy storage output power at the i-1th moment, J i represents the load current at the i-th moment, J i-1 represents the load current at the i-1th moment, R j represents the grid frequency at the jth moment, R j-1 represents the grid frequency at the j-1th moment, δ represents a very small positive number to prevent the denominator from being zero, λ represents the frequency disturbance fluctuation coefficient, and n represents the number of sampling periods in the abnormal stage.

[0065] The abnormal frequency linkage feature refers to the comprehensive response strength of the energy storage unit output power and load current to changes in grid frequency during periods of grid frequency anomalies (such as large fluctuations or exceeding the stable range). The larger the characteristic value, the more dramatic the response changes of the energy storage and load to the frequency anomaly and the closer the correlation; the smaller the value, the relative insensitivity or small change.

[0066] Q i Indicates the energy storage output power at the i-th moment (unit: kilowatt), J i represents the load current at the i-th moment (unit: ampere), R j represents the grid frequency at the jth moment (unit: Hz), δ is a very small positive number to prevent the denominator from being zero, which is set to 0.0001 here, λ is the frequency disturbance fluctuation coefficient, which is set to 2.4 in the experiment, and n is the number of sampling cycles in the abnormal stage, which is currently 4. The sampling data are as follows: the energy storage output power is 280, 290, 305, and 300 respectively; the load current is 420, 430, 440, and 435 respectively; the grid frequency is 49.8, 49.6, 49.4, and 49.7. After normalization, the energy storage power sequence is:

[0067] Q = [0.56, 0.58, 0.61, 0.60];

[0068] The load current sequence is:

[0069] J = [0.84, 0.86, 0.88, 0.87];

[0070] Calculate the molecular part, the distance between each set of sampling points is:

[0071] Group 1:

[0072]

[0073] Group 2:

[0074]

[0075] Group 3:

[0076]

[0077] The sum of the numerators is:

[0078] ∑=0.0283+0.0361+0.0141=0.0785;

[0079] Calculate the denominator part, the square difference between each frequency sampling segment is:

[0080] First paragraph: (49.6-49.8) 2 =0.04;

[0081] Second paragraph: (49.4-49.6) 2 =0.04;

[0082] Section 3: (49.7-49.4) 2 =0.09;

[0083] Adding δ = 0.0001, the denominator is:

[0084] ∑=0.04+0.04+0.09+0.0001=0.1701;

[0085] Substituting into the formula we get:

[0086]

[0087] The results show that during the frequency anomaly phase, there is a quantitative correlation between the synchronous variation amplitude of the energy storage output power and load current in the time series and the intensity of the frequency disturbance. The value GS = 1.1078 indicates that the energy storage system's joint response capability to abnormal grid frequency disturbances is at a strong level, reflecting that the site has high potential for energy storage regulation participation and load support during the current abnormal period. This value can be further used as a core reference for determining priority response sites in the subsequent scheduling output and load trend identification stages. It can also be used in the multi-station matching and sorting module to establish a response strength sorting mechanism to clarify the priority energy storage regulation targets under abnormal disturbance conditions.

[0088] See also Figure 3 , the energy storage scheduling output module includes:

[0089] The energy storage power screening submodule analyzes the output power, number, and available power of the energy storage units collected synchronously by each edge intelligent computing node based on the abnormal frequency linkage characteristics. It compares the current frequency change trend with the number power response characteristics, determines which energy storage units meet the response scheduling conditions, and screens the numbers participating in the scheduling to obtain the number power linkage set.

[0090] The edge computing nodes at each site collect the current output power, unique number and releasable power of each energy storage unit in real time. After the energy storage unit data of each site are uniformly summarized, the grid frequency change trend of the corresponding time period is extracted, and compared with the power response characteristics of the unit number during the historical frequency disturbance process. For example, if the output power of a certain numbered energy storage unit rapidly increases to more than 90% of the maximum output power within 5 seconds when the frequency drops, it is determined that the unit has a fast response capability. If the power change rate is less than 30% of the maximum output power per second, it is considered to have a weak response capability. The high response capability unit is divided to set the threshold, and the current frequency change direction is further judged. If the frequency is decreasing, the energy storage units with outstanding power responses under frequency decrease conditions are selected and included in the dispatchable candidate set. Conversely, if the frequency is increasing, the energy storage units that can actively reduce power output under frequency increase conditions are matched. The process of judging whether each energy storage unit meets the current dispatch conditions also requires comparing the output power response time with the duration of the frequency change trend. For example, if the frequency decrease lasts for 10 seconds, and the response time of a certain energy storage unit is greater than 15 seconds, this unit will be excluded and will not enter the dispatch response judgment. Then, all energy storage units that meet the above judgment conditions are screened and output to form a power linkage set of units that meet the frequency trend response conditions.

[0091] The releasable power determination submodule calculates the releasable power corresponding to each energy storage unit based on the numbered power linkage set, analyzes the required release capacity during the current peak and valley operation phase, selects energy storage numbers that meet the energy supply conditions, optimizes the energy storage unit classification, and obtains the dispatchable energy storage number set;

[0092] The current releasable power value of each energy storage unit in the set is read one by one. The value is obtained by multiplying the current state of charge of the energy storage unit by its total capacity minus the minimum protection power. For example, if the total capacity of a battery is 100kWh, the current state of charge is 85%, and the minimum protection power is 10kWh, then the releasable power is 75kWh. Then the dispatch system determines whether the current operating period is a peak period or a valley period. If it is in a peak period, it is necessary to output power to support high power load. If it is in a valley period, the dispatch target is converted to energy storage charging. The minimum energy supply capacity required for dispatching during the peak period is set to 300kWh. If the energy storage unit in the current linkage set If the total amount of releasable electricity is less than 300kWh, some energy storage units with a releasable electricity of less than 20kWh need to be eliminated, and the numbered units that have not entered the set but have medium response capabilities and can release more than 30kWh need to be re-searched to supplement the set. In addition, during the judgment process, it is also necessary to determine whether there is a risk of over-discharge based on the rate of change of electricity of each energy storage number in the past 30 minutes. If the rate of decrease of electricity exceeds twice its average rate, it will not be included in the dispatchable objects. After re-screening the energy storage units in the set through this process, the energy storage numbers that meet the energy supply threshold requirements will be regrouped and output by number to obtain the set of dispatchable energy storage numbers that can participate in scheduling in the current stage.

[0093] The dispatch instruction generation submodule compares the output power, releasable power and frequency response of each number based on the dispatchable energy storage number set, optimizes the number priority, and uses the formula:

[0094]

[0095] Calculate the unified output amplitude SR of energy storage, adjust the output mode of each number, issue dispatch instructions and monitor the response of each energy storage unit to obtain the dispatched energy storage output, where PR z Represents the output power of the zth energy storage unit, ER z Represents the releasable amount of energy in the zth energy storage unit, Rf z Represents the grid frequency currently monitored by the zth energy storage unit, Rf s Represents the reference frequency of the current stage, n SR Represents the total number of energy storage units participating in the aggregation in this dispatch.

[0096] The unified output amplitude of energy storage refers to the total energy release intensity that can be output by the overall energy storage system when multiple energy storage units cooperate in peak-valley scheduling. The system comprehensively considers the current output power, releasable power and real-time response characteristics of each energy storage unit to changes in grid frequency, and weights and summarizes all energy storage units with scheduling capabilities according to priority. It reflects the energy release level that can be uniformly output to the outside by all currently dispatchable energy storage units after coordinated action based on their respective states and system requirements. It is a key comprehensive indicator for system scheduling decisions and issuing output instructions.

[0097] Compare the output power, available power and current response status of the grid frequency corresponding to each number. It is necessary to process each parameter in a unified dimension and calculate the unified output amplitude SR of the energy storage and the output power parameter PR. z Indicates the power level that each number can provide at the moment, and the amount of power that can be released ER z Indicates the remaining discharge capacity of the numbered device, and the grid monitoring frequency Rf z The frequency data collected by the current device, reference frequency Rf s The frequency is uniformly set to 50Hz, with the goal of conventional stable operation of the power system as the benchmark. In order to maintain the comparability of various physical quantities under a unified scale, normalization processing is required. Here, the normalized values ​​are directly quoted in the calculation, as follows:

[0098] For energy storage number A1, PR z =120, ER z =400, Rf z =49.8, and after normalization, they are 0.800, 0.842, and 0.496 respectively; for A3, PR z =95, ER z =300, Rf z =49.5, and after normalization, they are 0.633, 0.707, and 0.492 respectively; for A4, PR z =130,ER z =450、Rf z =50.0, after normalization they are 0.867, 0.894, and 0.500 respectively. Substituting into the formula:

[0099] A1:

[0100] A3:

[0101] A4:

[0102] The calculation results are:

[0103] SR≈0.731+0.528+0.820=2.079;

[0104] This value represents the relative strength of the calculated unified output amplitude of the currently dispatchable energy storage devices. Under a normalized benchmark, if the dispatch threshold is set to 2, the current result of 2.079 is higher than the threshold, indicating that the dispatch conditions are met. The system will adjust the output status of each numbered energy storage device in order of priority and issue corresponding power instructions item by item, which serve as the input basis for subsequent load trend identification and dynamic allocation. The formula introduces a dynamic suppression mechanism for device synchronization by using frequency deviation as the denominator adjustment term, and combines the product of power and electricity to construct a multivariate indicator of output potential, avoiding the dispatch deviation caused by a single weight mechanism, thereby improving the overall control accuracy and interpretability.

[0105] See also Figure 4 , the load trend identification module includes:

[0106] The load trend acquisition submodule analyzes the site's power load monitoring data based on the scheduled energy storage output, compares the changes in power load curves over consecutive time periods, and screens the load trend segments with key fluctuations within the cycle to obtain the key load fluctuation curve;

[0107] Read the energy storage output power corresponding to each site and the power load monitoring data collected by the load sensor during the time period, summarize the load data of each site into a time series per minute, and form a load change array of 60 hours in length. Then divide it into an analysis cycle every 5 minutes, and generate a total of 12 cycle segments. In each segment, calculate the difference between the starting value and the ending value of the power load, and compare it with the difference between the maximum load value and the minimum load value in the segment to determine the severity of the load change in the segment. Set the benchmark value for judging fluctuations to 10% of the maximum load value. If the difference between the maximum and minimum values ​​in a segment exceeds the benchmark value, and the net change rate from the start to the end exceeds 5%, it is judged that To identify critical load fluctuations, a complete load trend curve is extracted from the segments identified as fluctuations, and the rising, falling, and violently fluctuating segments of the current load are sorted out in chronological order. Combined with the records of changes in the dispatched energy storage output power, a comparison is made to see whether the increase in energy storage power occurs synchronously in the load rising segment within the same time period. If the energy storage output increases from 20kW to 40kW within 5 minutes, and the load increases from 200kW to 240kW at the same time, it is recorded as a trend-consistent segment. If there are two or more trend-consistent segments, the load trend curve of the site for that hour is marked as a critical curve. By integrating the change direction, amplitude, and continuity of the key segments, the key load fluctuation curve of each site is output.

[0108] The abnormal trend screening submodule determines the change of trend curves of each site based on the key load fluctuation curve, compares and finds sites with key abnormal change rates, optimizes and sorts the trend curves of the top two sites, and obtains the site abnormal amplitude sequence;

[0109] Each site curve is compared segment by segment, and the rate of change value is extracted. The load increase or decrease per minute is used as the basis for calculating the rate of change. The maximum rate of change within each 5-minute segment of 60 minutes is extracted and used as the representative rate for that period. The maximum rate values ​​in the 12 segments of each site are then sorted, and the segment with the maximum rate is selected as the outlier segment. A determination is made as to whether the rate exceeds the set abnormal rate baseline of 20 kW / min. If so, it is marked as a key outlier. If the rate of more than three segments at a site is found to be greater than this threshold, the entire site is considered an outlier. The maximum rates of all outlier sites are re-sorted from high to low, and the top two sites are selected as trend-focused sites. The trend curves corresponding to these two sites are refined, and the combined structure of the turning points, steep rise and fall sections, and slow-changing regions in the curves is extracted. The rate of change of all key segments is re-aggregated to output an integrated rate sequence, and an abnormal amplitude sequence for the site is constructed in chronological order.

[0110] The variation interval calculation submodule compares the trend amplitudes of the first two stations in the same period based on the station abnormal amplitude sequence, analyzes the difference in load changes between the corresponding numbers of the two stations, determines the boundary of the variation interval, and obtains the station load variation interval;

[0111] Read the abnormal rate sequences of the top two sites, and pair them time period by time. Compare the difference of the change rates within the same cycle, extract the time points where the difference is greater than 10kW / min and mark them, and then extract the corresponding load change values ​​at the time points. Extract the start value and end value of the time period from the load curves of the two sites respectively, compare their changes, and further judge whether the changes are in the normal fluctuation range. Set the load normal fluctuation range threshold to ±30kW. If the load change difference between the two sites exceeds this value, it will be recorded as a strong change segment in the cycle. Continue to mark the strong change segment according to the start and end time to define the The segment is the key load change interval. The difference between the base load value and the upper load limit of the corresponding numbered two sites is then checked to determine whether there is a capacity redundancy difference. For example, the base load of site A01 is 150kW, the upper limit is 200kW, and the current maximum value of the fluctuation segment is 195kW. The base load of site B03 is 140kW, the upper limit is 220kW, and the current maximum value is 180kW. The difference is significant. Based on the joint judgment of the difference in change rate and the difference in load change amplitude within each cycle, the maximum, minimum, and duration of the two sites in the key segment are extracted to output the complete site load change interval.

[0112] See also Figure 5 , the multi-station matching sorting module includes:

[0113] The energy storage load comparison submodule compares the current remaining energy storage at each site with the electricity load during the same period based on the site load variation range. It calculates the difference between the energy storage and electricity load during the same period, and then synchronously normalizes the maximum load variation and the energy storage difference to obtain the energy storage load adaptation difference.

[0114] Extract the current remaining energy storage value of each site in the interval and the average power load in the corresponding time period, pair the remaining energy storage value with the load value site by site, and perform a difference calculation operation. The difference is the remaining energy storage minus the average load. If the difference is positive, it means that the site has load carrying capacity. If it is negative, it means that the current energy storage cannot cover the corresponding load demand. For example, the remaining energy storage of site A is 180kWh, and the load in the same period is 150kWh, with a difference of 30kWh; the remaining energy storage of site B is 120kWh, and the load in the same period is 170kWh, with a difference of -50kWh. Then extract the maximum load change amplitude of the site in the current change interval, which is defined as the maximum increase or decrease in load per unit time of the site, and then normalize the maximum change value with the aforementioned energy storage and load difference. The normalization process is based on the maximum load change amplitude of all sites. The data is scaled to the range of 0 to 1, and the corresponding energy storage difference is processed in the same way. During the normalization process, the minimum benchmark value is set to the smallest negative load change value among all sites, and the maximum benchmark value is set to the maximum positive change value. After normalization, the absolute value of the difference between the two normalized indicators is taken and then compared and evaluated. For example, the normalized load change amplitude of site A is 0.75, and the normalized energy storage difference is 0.85, with a difference of 0.10. The corresponding values ​​of site B are 0.95 and 0.40, with a difference of 0.55. This indicates that the adaptation difference of site B is significantly high. According to the preset adaptation difference interval classification standard, a difference in the range of 0.0-0.2 is defined as high adaptation, 0.2-0.4 as moderate adaptation, and more than 0.4 as severe adaptation deviation. According to this classification standard, the adaptation differences of all sites are classified and sorted, and the energy storage load adaptation difference amplitude corresponding to each site is output.

[0115] The matching degree calculation submodule calculates the load change rate and energy storage release rate of the site at the same time based on the difference in energy storage load adaptation, and compares the load distribution differences between sites using the formula:

[0116]

[0117] Get the matching level between sites, where MD o Indicates the matching level of site o, DE o represents the remaining energy storage at site o, DLo Indicates the current power load of site o, DR o Indicates the energy storage release rate of site o within a specified period, DV o represents the load change rate of site o during a specified period, D ov represents the load distribution difference between site o and site v, n MD Indicates the total number of sites involved in the matching analysis;

[0118] The inter-site matching level refers to an indicator used to measure the adaptability between a site's energy storage and load under the combined effects of multiple parameters such as the current remaining energy storage, power load, energy storage release rate, load change rate, and load distribution differences with other sites. A larger value indicates poorer adaptability between the site's energy storage and load, greater supply and demand differences between sites, and a relatively lower priority.

[0119] Calculate the load change rate and energy storage release rate of the site in the same period, extract the two rates as the power change amplitude in the time segment divided by the duration of the period, and form the dynamic response magnitude within the cycle. Further, according to the load distribution of the site area, calculate the load mean difference between it and other sites in the same scheduling cycle, and square the difference to express the distribution difference of the site in the global scheduling. After completing the dimension conversion for each parameter, directly introduce the normalized result into the formula calculation. Taking site A as an example, its remaining energy storage DE o is 220, the current power load DL o is 140, and the square difference is calculated to get (DE o -DL o ) 2 =(220-140) 2 =6400, the energy release rate DR of site A in the current cycle o is 18, the load change rate DV o is 6, and after square and root processing we get:

[0120]

[0121] The load distribution differences between this site and other sites in the same period are 12, 10, and 14. The squares and sums of these differences yield:

[0122]

[0123] Substituting into the formula:

[0124]

[0125] The results show that in the current cycle, site A has sufficient energy storage resources, relatively moderate power load, high energy storage release efficiency and load regulation response capabilities. At the same time, under the condition that the load distribution difference with other sites is small, it shows good supply and demand matching stability as a whole. The calculated matching level MD o ≈14.56 is at a relatively low level among all currently participating stations, indicating that its comprehensive dispatching capability is relatively strong and it is suitable for priority entry into the target screening range of multi-station dispatching.

[0126] The priority sorting submodule analyzes the matching degree and site number of each screening site based on the matching degree level between sites, adjusts the priority one by one according to the matching degree level, optimizes the arrangement order of matching sites, and obtains the priority site matching set;

[0127] Obtain the adaptation difference amplitude of all sites in the previous step, and associate each site number with its matching degree one by one, and determine whether the matching degree level of each site falls into the high, medium, and low ranges. Set the matching degree classification standard as follows: the adaptation difference amplitude is less than 0.2 for high level, 0.2 to 0.4 for medium level, and greater than 0.4 for low level. Then, filter all site numbers in the high level and sort them from high to low according to the remaining energy storage. If there are multiple sites in the high level with the same remaining energy storage or a difference of less than 5kWh, further sort them according to The current load stability is judged. The load stability is based on the average load change rate of no more than 5% in three consecutive cycles as a stable reference value. If the change rate of a site is 3% and that of another site is 6%, the former is ranked higher. The same sorting rules are executed for the medium-level sites, and only the high-level sorting results are arranged before the medium-level results. For low-level sites, only the numbers are retained for reference and are not included in the matching queue. After all sorting is completed, a site priority list is formed. For each site in the list, the number, remaining energy storage, adaptation difference, and sorting position are recorded, and the matching set of the priority sites is output.

[0128] See also Figure 6 , the load dynamic distribution module includes:

[0129] The priority site identification submodule analyzes the ranking order of each site and the current energy storage output and load distribution based on the priority site matching set. It determines the site with the best priority and whose energy storage and load characteristics meet the regulation conditions. It also compares the priority and controllability of multiple sites to obtain the target site number for regulation.

[0130] The ranking position, current remaining energy storage capacity, actual output power, and power load value for each site in the matching set are read. The system then determines whether the current ranking has been in the top two for three consecutive rounds and whether the load stability is high. As one of the initial screening criteria, stability is determined based on load fluctuations within a set threshold of 5% over three consecutive cycles. Sites with remaining energy storage capacity exceeding the load are screened, prioritizing sites with a storage difference greater than 20 kWh for the next round of evaluation. The offset between each site's current energy storage output and current load is then calculated. The ratio of actual energy storage output power to power load is used as a reference for the control ratio. If this ratio is between 0.8 and 1.2, the control capability is considered controllable. If it deviates from this range, the control capability is marked as weak. A baseline offset value of 0.2 is set. The site with the smallest offset is determined as the site with the best control capability. Through multiple rounds of comparison of ranking position, controllability indicators, and load synchronization capabilities, the site with the highest priority and the smallest control offset is identified as the target for this round of control. The corresponding site number is extracted, and the target site number is output.

[0131] The energy storage mode adjustment submodule analyzes the current energy storage output mode and energy storage response characteristics of the target site according to the sequence number of the control target site, optimizes the adjustment mode to adapt to the real-time load distribution, and compares the historical energy storage response with the current state changes to determine the optimal energy storage output mode and obtain the energy storage output type;

[0132] According to the target site number, the current energy storage unit working mode of the site is extracted, including three states: charging, discharging, and maintaining output. Combined with the corresponding power output change record of the site, it is judged whether the power change direction is consistent with the load change direction within a 10-minute period. If there are more than 3 reverse changes or the response delay exceeds 2 minutes, it is determined that the current energy storage output mode is not compatible with the load change. The energy storage response characteristics of this site are statistically analyzed, including the correspondence between the energy storage output power adjustment speed, output duration and frequency interference. The response delay threshold is set to 2 minutes. If the adjustment delay is higher than this value and the output fluctuates frequently, it is carried out. Recommendations for switching energy storage output modes include adjusting continuous output to intermittent segmented output, or setting a minimum output lock interval during the frequency disturbance phase. Historical comparisons are conducted to extract the energy storage operation records for the site during the same time period the previous day. Analysis is conducted to determine whether the energy storage control method used at the time was more stable under similar load conditions. If the historical energy storage power output deviation is lower than the current solution, the historical control mode is restored first. If the historical control fluctuation amplitude is greater than the current state, the current solution is maintained, and the strategy is forced to switch only after the output fluctuation lasts for more than 15 minutes. Finally, the output mode that best matches the current load distribution is determined, and the energy storage output type that the site should currently adopt is output.

[0133] The load amplitude calculation submodule analyzes the load distribution data before and after the adjustment based on the energy storage output type, calculates the load change range caused by the energy storage mode adjustment, compares the load distribution shift caused by the adjustment, screens and determines the degree of load redistribution, and obtains the load distribution change amplitude;

[0134] Read the electricity load records for each site within 5 minutes before and after the energy storage adjustment. Compare the total electricity load value and the load increase or decrease values ​​for each minute before and after the adjustment. Identify whether any load transfer to other sites occurred during this process. By reading the synchronous load fluctuation data of multiple sites, determine whether there is cross-site load compensation caused by the regulation of the local site. If the load change direction of other sites is opposite to that of the regulated site and the change exceeds 5kW, it is considered that the load has been redistributed. The amplitude value of the distribution point is extracted. The load increase and decrease of all relevant sites within the adjustment window are counted and the absolute values ​​are accumulated to calculate the total load change range. This total change value is then compared with the original load change value of the adjusted site. The redistribution ratio threshold is set to 30%. If more than 30% of the total change is borne by other sites, the adjustment is considered a high-amplitude load distribution operation. If the ratio is between 15% and 30%, it is considered a medium-amplitude load distribution operation, and if it is less than 15%, it is considered a slight load distribution operation. The load distribution change amplitude caused by the energy storage output adjustment is output.

[0135] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The edge intelligent computing-enabled peak-valley energy storage and charging system is characterized by: The system comprises: The grid frequency identification module analyzes the output power, remaining capacity, and load current changes of the energy storage unit based on the grid frequency signals synchronously collected by the edge intelligent computing nodes at each site. It compares the trends of frequency and energy storage parameters, determines the linkage characteristics, and monitors the abnormal recovery process to obtain the abnormal frequency linkage characteristics. The energy storage scheduling output module screens the output power, releasable power and number of the energy storage units based on the abnormal frequency linkage characteristics, determines whether the releasable power meets the peak and valley energy supply conditions, sorts the numbers and issues output instructions, and calculates the power and current of the energy supply units to obtain the scheduled energy storage output; The load trend identification module compares the power load of each site with the load change trend over a period of time based on the scheduled energy storage output, screens sites with abnormal load change trends, sorts the load detection data of the abnormal sites, analyzes the difference in load changes between the top two sites in the sorting, and obtains the site load change range; The multi-station matching sorting module calculates the remaining energy storage and power load of the site based on the site load variation range, compares the matching relationship, sorts the sites by priority according to the matching degree, dynamically adjusts the sorting structure, and obtains a priority site matching set.

2. The edge intelligent computing enabled peak-valley energy storage and charging system according to claim 1 is characterized in that: The abnormal frequency linkage characteristics include the disturbance type category, response peak amplitude, and linkage duration period; the dispatched energy storage output includes the energy storage output amplitude, the dispatch period identifier, and the dispatch unit set; the site load change interval includes the maximum change in the interval, the change rate level, and the associated site sequence number; the priority site matching set includes the sorting weight parameter, the site priority identifier, and the matching sequence number.

3. The edge intelligent computing enabled peak-valley energy storage and charging system according to claim 1 is characterized in that: The grid frequency determination module includes: The frequency trend extraction submodule analyzes the time series data of energy storage unit output power, remaining capacity, and load current collected synchronously at each site based on the grid frequency signals collected synchronously by the edge computing nodes at each site. By calculating the amplitude and direction of frequency changes at each time node and comparing the relationship between frequency trends and energy storage output during the difference phases, the difference in the power-frequency linkage trend is determined. The energy storage status linkage analysis submodule analyzes the changes in energy storage output power, remaining capacity, and load current at the same site over multiple cycles based on the differences in the frequency-power linkage trends. It calculates the directional consistency of each indicator and determines the energy storage condition linkage interval range by screening the periodic data that meets the conditions. The frequency anomaly evolution detection submodule analyzes the frequency data sequence based on the amplitude of the energy storage condition linkage interval, determines the continuous changes in the energy storage output power and load current between the start and end points of the abnormal phase, calculates the response of the energy storage power and load current to the frequency disturbance, and obtains the abnormal frequency linkage characteristics by combining the disturbance type and period characteristics.

4. The edge intelligent computing enabled peak-valley energy storage and charging system according to claim 1 is characterized in that: The energy storage scheduling output module includes: Based on the abnormal frequency linkage characteristics, the energy storage power screening submodule analyzes the output power, number, and available power of the energy storage units synchronously collected by each edge intelligent computing node, compares the current frequency change trend with the number power response characteristics, determines which energy storage units meet the response scheduling conditions, and screens the numbers participating in the scheduling to obtain the number power linkage set; The releasable power determination submodule calculates the releasable power corresponding to each energy storage unit based on the numbered power linkage set, analyzes the release capacity required in the current peak and valley operation phase, screens energy storage numbers that meet the energy supply conditions, optimizes the energy storage unit classification, and obtains the dispatchable energy storage number set; The dispatch instruction generation submodule compares the output power, releasable power and frequency response of each number based on the dispatchable energy storage number set, optimizes the number priority, calculates the unified output amplitude of the energy storage, adjusts the output mode of each number, issues dispatch instructions and monitors the response of each energy storage unit to obtain the dispatched energy storage output.

5. The edge intelligent computing enabled peak-valley energy storage and charging system according to claim 1 is characterized in that: The load trend identification module includes: The load trend acquisition submodule analyzes the site's power load monitoring data based on the dispatched energy storage output, compares the changes in power load curves over consecutive time periods, and screens the load trend segments with key fluctuations within the cycle to obtain a key load fluctuation curve; The abnormal trend screening submodule determines the change of trend curves of each site based on the key load fluctuation curve, compares and finds sites with key abnormal change rates, optimizes and sorts the trend curves of the top two sites, and obtains the site abnormal amplitude sequence; The variation interval calculation submodule compares the trend amplitudes of the first two sites in the same period according to the site abnormal amplitude sequence, analyzes the difference in load changes between the corresponding numbers of the two sites, determines the boundary of the variation interval, and obtains the site load variation interval.

6. The edge intelligent computing enabled peak-valley energy storage and charging system according to claim 1 is characterized in that: The multi-station matching and sorting module includes: The energy storage load comparison submodule compares the current remaining energy storage at each site with the electricity load during the same period based on the site load variation interval, calculates the difference between the energy storage and electricity load during the same period, and then synchronously normalizes the maximum load variation amplitude and the energy storage difference to obtain the energy storage load adaptation difference amplitude; The matching degree calculation submodule calculates the load change rate and energy storage release rate of the site during the same period based on the energy storage load adaptation difference, compares the load distribution differences between the sites, and obtains the matching degree level between the sites; The priority sorting submodule analyzes the matching degree and site number of each screening site based on the matching degree level between the sites, adjusts the priority one by one according to the matching degree level, optimizes the arrangement order of the matching sites, and obtains a priority site matching set.

7. The edge intelligent computing enabled peak-valley energy storage and charging system according to claim 1 is characterized in that: The system further comprises: The load dynamic distribution module analyzes the energy storage output and load distribution of the target site based on the priority site matching set, determines that the priority site is the regulation object, adjusts the energy storage output mode, compares the load distribution changes before and after the adjustment, and obtains the load distribution change amplitude; The load distribution change range includes the adjusted load value, the distribution target site, and the distribution batch number.

8. The edge intelligent computing enabled peak-valley energy storage and charging system according to claim 7 is characterized in that: The load dynamic distribution module includes: The priority site identification submodule analyzes the ranking order of each site and the current energy storage output and load distribution based on the priority site matching set, determines the site with the best priority and whose energy storage and load characteristics meet the regulation conditions, and compares the priorities and controllability of multiple sites to obtain the target site number for regulation; The energy storage mode adjustment submodule analyzes the current energy storage output mode and energy storage response characteristics of the control target site according to the control target site sequence number, optimizes the adjustment mode to adapt to the real-time load distribution, and compares the historical energy storage response with the current state change to determine the optimal energy storage output mode and obtain the energy storage output type; The load amplitude calculation submodule analyzes the load distribution data before and after the adjustment based on the energy storage output type, calculates the load change range caused by the energy storage mode adjustment, compares the load distribution shift caused by the adjustment, screens and determines the degree of load redistribution, and obtains the load distribution change amplitude.

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