Optimization method and system for cooperation of light storage and charging based on intelligent scheduling

Abstract

The application provides a light storage and charging cooperation optimization method and system based on intelligent scheduling, and relates to the technical field of energy scheduling. The method comprises the following steps: for a target power grid area, constructing a local intelligent agent cluster, collecting power grid data, performing gain processing according to a PGA module, and executing double-path transmission of ADC and a high-speed comparator and power grid coordination control; the high-speed comparator performs dynamic setting of a high-speed comparator threshold value based on power data, and performs protection driving of power grid fluctuations; the ADC converts the power grid data into digital data, and performs light storage and charging coordination management of the power grid through upper scheduling analysis of an extreme controller in the power grid central control and the local intelligent agent cluster. The application solves the technical problems of slow real-time response, prediction error affecting scheduling accuracy and reliability in the prior art, can obtain coordination optimization of light storage and charging equipment, and improves the real-time performance, accuracy and reliability of power grid scheduling.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of energy scheduling, in particular to a photovoltaic storage and charging cooperation optimization method and system based on intelligent scheduling. BACKGROUND

[0002] With the rapid development of photovoltaic power generation, energy storage systems and charging loads, photovoltaic storage and charging coordinated scheduling has become an important means to realize grid stability and energy optimization. The existing photovoltaic storage and charging scheduling method mainly uses centralized scheduling or prediction-based optimization algorithm to balance the grid load by unified planning of photovoltaic output, energy storage state and charging load. However, the centralized scheduling method has the problems of high computational complexity, large data transmission delay and insufficient real-time response capability, and is difficult to adapt to the grid environment with frequent fluctuations of photovoltaic output and load. In addition, the existing prediction-based scheduling method highly depends on the accuracy of photovoltaic power generation prediction and the accuracy of load prediction, and prediction errors will lead to scheduling deviation and energy waste.

[0003] The existing photovoltaic storage and charging scheduling method has the technical problems of slow real-time response, prediction error affecting scheduling accuracy and reliability. SUMMARY

[0004] The purpose of the present application is to provide a photovoltaic storage and charging cooperation optimization method and system based on intelligent scheduling, which solves the technical problems of slow real-time response, prediction error affecting scheduling accuracy and reliability in the existing photovoltaic storage and charging scheduling method.

[0005] In view of the above problems, the present application provides a photovoltaic storage and charging cooperation optimization method and system based on intelligent scheduling.

[0006] The first aspect of the present application provides a photovoltaic storage and charging cooperation optimization method based on intelligent scheduling, which comprises: for a target grid area, constructing a local intelligent agent cluster by grouping photovoltaic storage and charging nodes under the jurisdiction of a feeder and building an intelligent agent unit, wherein each grid feeder corresponds to an intelligent agent unit; collecting grid data, performing gain processing according to a PGA module, and executing double-path transmission of ADC and high-speed comparator and grid coordination control; wherein the high-speed comparator performs dynamic setting of the high-speed comparator threshold value based on power data to protect and drive the grid fluctuation; and the ADC converts the grid data into digital data to perform photovoltaic storage and charging coordination of the local intelligent agent cluster and the upper scheduling analysis of the grid central control.

[0007] Optionally, the charging device, energy storage device and photovoltaic device are quantified in a unified dimension of schedulable capacity; wherein the quantification method includes: for the charging device, taking the battery state and flexible charging time window and power range as the first metric standard; for the energy storage device, taking the remaining charge and discharge capacity and cycle life cost as the second metric standard; for the photovoltaic device, taking the predicted output curve as the third metric standard; the first metric standard-first schedulable capacity, the second metric standard-second schedulable capacity, and the third metric standard-third schedulable capacity are used to quantify the photovoltaic storage charging device covered by the target power grid area, and added to the scheduling baseline library.

[0008] Optionally, a photovoltaic storage charging collaborative mode is set, wherein the photovoltaic storage charging collaborative mode is defined by photovoltaic priority local consumption and complementary smoothing; the photovoltaic storage charging topology of the target power grid area is obtained, and a local agent cluster is constructed, wherein the mapping of the photovoltaic storage charging topology-feeder attribution-local agent cluster is used as the scheduling optimization architecture, the same power grid feeder is used as an agent unit, and the scheduling baseline library is used as the construction basis of the agent unit.

[0009] Optionally, for the photovoltaic storage charging topology, a dedicated PGA channel is integrated in each charging node, energy storage node and photovoltaic node; for each dedicated PGA channel, a first thread is connected to an ADC, and a second thread is connected to a high-speed comparator.

[0010] Optionally, the sensor module collects power grid data, which is transferred to the dedicated PGA channel for gain processing to determine gain power grid data; wherein the gain processing steps include: transmitting the power grid data of the first node to the corresponding first dedicated PGA channel, if the sensing signal strength is less than the preset standard interval, performing gain up; if the sensing signal strength is greater than the preset standard interval, performing gain down.

[0011] Optionally, the gain power grid data is transmitted in dual channels, the ADC receives the gain power grid data and writes it into the minimalist controller of the power grid central control; the high-speed comparator receives the power data in the gain power grid data and dynamically sets the threshold value of the high-speed comparator according to the power level.

[0012] Optionally, the power level and the threshold value of the high-speed comparator are linearly positively correlated; if it is a high-power mode, a high overcurrent protection threshold value is used to dynamically set the high-speed comparator associated with the power grid sensing position; if it is a low-power precision mode, a low overcurrent protection threshold value is used to dynamically set the high-speed comparator associated with the power grid sensing position.

[0013] Optionally, according to the minimal controller of the grid control center, a multi-threaded upper scheduling signal is generated and broadcasted to the target grid area, wherein the upper scheduling signal at least includes a high-energy factor signal and a storage factor signal, the high-energy factor signal is a discharge incentive, and the storage factor signal is a charging incentive; the local intelligent agent cluster receives the upper scheduling signal of the directional thread, each intelligent agent unit executes heuristic light storage and charging coordination of the feeder range, and generates a light storage and charging scheduling instruction.

[0014] Optionally, the first intelligent agent unit receives the first upper scheduling signal, matches the light storage and charging nodes in the first feeder in the scheduling baseline library, each light storage and charging node executes heuristic scheduling decision to determine a light storage and charging action instruction, and the light storage and charging action instruction is subjected to conflict judgment and resolution to generate a first light storage and charging scheduling instruction, wherein the first light storage and charging scheduling instruction executes light storage and charging coordination scheduling in the first feeder.

[0015] In a second aspect of the present application, a light storage and charging coordination optimization system based on intelligent scheduling is provided, comprising: an intelligent agent cluster construction module, configured to construct a local intelligent agent cluster by grouping light storage and charging nodes under the ownership of a feeder and building intelligent agent units for a target grid area, wherein each grid feeder corresponds to an intelligent agent unit; a task execution module, configured to collect grid data, perform gain processing based on a PGA module, execute dual-channel transmission of an ADC and a high-speed comparator, and coordinate and control the grid; wherein the high-speed comparator performs dynamic setting of a high-speed comparator threshold based on power data to protect and drive the grid fluctuation; and a coordination management module, configured to convert the grid data into digital data by the ADC, perform light storage and charging coordination of the local intelligent agent cluster based on upper scheduling analysis of a minimal controller in the grid control center, and manage light storage and charging coordination of the grid.

[0016] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0017] The method provided by the embodiments of the present application is aimed at a target grid area, and constructs a local intelligent agent cluster by grouping light storage and charging nodes under the ownership of a feeder and building intelligent agent units, wherein each grid feeder corresponds to an intelligent agent unit; collects grid data, performs gain processing based on a PGA module, executes dual-channel transmission of an ADC and a high-speed comparator, and coordinates and controls the grid; wherein the high-speed comparator performs dynamic setting of a high-speed comparator threshold based on power data to protect and drive the grid fluctuation; and the ADC converts the grid data into digital data, performs light storage and charging coordination of the local intelligent agent cluster based on upper scheduling analysis of a minimal controller in the grid control center, and manages light storage and charging coordination of the grid, thereby achieving the technical effect of optimizing the coordination of light storage and charging equipment, improving the real-time performance, accuracy and reliability of grid scheduling.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the optimization method for the optical storage-charging coordination based on intelligent scheduling provided in this application.

[0021] Figure 2 This is a schematic diagram of the structure of the intelligent scheduling-based photovoltaic-storage-charging optimization system provided in this application.

[0022] Figure labeling: Intelligent agent cluster construction module 11, task execution module 12, cooperation management module 13. Detailed Implementation

[0023] This application provides a method and system for optimizing the coordination of photovoltaic, energy storage, and charging (PV-SGC) scheduling based on intelligent dispatching. This method addresses the technical problems of slow real-time response and prediction errors affecting dispatching accuracy and reliability in existing PV-SGC-charging dispatching methods. It achieves the technical effect of coordinating and optimizing PV-SGC-charging equipment, thereby improving the real-time performance, accuracy, and reliability of power grid dispatching.

[0024] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0025] Example 1, as Figure 1 As shown, this application provides a method for optimizing the coordination of optical storage and charging based on intelligent scheduling. The method includes:

[0026] For the target power grid area, a local agent cluster is constructed by grouping the light storage and charging nodes under the feeder attribution and building agent units, wherein each power grid feeder corresponds to an agent unit.

[0027] Specifically, in the target power grid area, the light storage and charging devices, including photovoltaic power generation devices, energy storage devices and charging devices, are grouped according to the feeder attribution of the power grid. The feeder refers to the distribution line from the transformer substation in the power grid, and each feeder covers a certain area of load and distributed energy. The topology information of the target power grid area is obtained, each light storage and charging node is identified to belong to a feeder, and is classified according to the node type and function, for example, the photovoltaic node focuses on power output prediction, the energy storage node focuses on residual capacity and cycle life, and the charging node focuses on flexible charging time and power range.

[0028] After completing the feeder attribution identification, an agent unit is created for each power grid feeder. The agent unit is the smallest control unit of local scheduling, which is used to manage the light storage and charging devices in the corresponding feeder, and realizes local decision and collaborative control. In the process of building the agent unit, the schedulable capacity information of each light storage and charging node is loaded into the corresponding agent unit, including photovoltaic predicted output, energy storage available capacity, charging flexibility and other indicators. According to the grouping of light storage and charging nodes under the feeder attribution and the building of agent units, a local agent cluster is formed, which takes the feeder as a unit. Each agent unit can independently execute light storage and charging scheduling, and can also realize multi-level collaboration.

[0029] Further, before building the agent unit, the schedulable capacity of the charging device, the energy storage device and the photovoltaic device is quantified under the unified dimensioning, wherein the quantification method includes: for the charging device, the battery state, the flexible charging time window and the power range are taken as the first dimensioning standard; for the energy storage device, the residual charge and discharge capacity and the cycle life cost are taken as the second dimensioning standard; for the photovoltaic device, the predicted output curve is taken as the third dimensioning standard; the first dimensioning standard-the first schedulable capacity, the second dimensioning standard-the second schedulable capacity, and the third dimensioning standard-the third schedulable capacity are used to quantify the light storage and charging devices covered by the target power grid area, and added to the scheduling baseline library.

[0030] Specifically, between the building of the agent unit, the schedulable capacity of the light storage and charging devices in the target power grid area, i.e. the charging device, the energy storage device and the photovoltaic device, is quantified under the unified dimensioning. The schedulable capacity refers to the power capacity or energy contribution capacity of each device that can be used for power grid scheduling within a certain time range.

[0031] The specific quantification manner of the schedulable capacity quantification includes: for a charging device, such as a charging pile or a charging unit, the battery state, the flexible charging time window and the allowed power range of the charging device are taken as the first metric. The battery state can be reflected by SOC, indicating the current remaining power of the battery, and determines the size of the power that the charging device can receive or provide. For example, when the battery is seriously aged, the acceptable charging power will decrease, and the discharging capacity will also weaken. The flexible time window refers to the schedulable charging start and end time period, and in different time periods, the charging power that the charging device can accept may be different. For example, during the off-peak period of the power grid load, the charging device can charge at a larger power. The power range refers to the upper and lower limits of the power rate of the charging device in the normal working state. By comprehensively considering the battery state, the flexible charging time window and the allowed power range, the maximum power that the charging device can provide or absorb in the current time period can be calculated, forming the first schedulable capacity, which reflects the degree of participation of the charging device in the current state in the grid scheduling. The first schedulable capacity Q1 is expressed as: wherein Q1 represents the maximum power range that the charging device can provide or absorb in the current time period, t start and t end are the start and end times of the flexible charging time window respectively, and represent the lower and upper limits of the power of the charging device when the battery state is SOC and the time is t.

[0032] For a storage device, the remaining chargeable and dischargeable capacity and the cycle life cost of the storage device are taken as the second metric, wherein the remaining chargeable and dischargeable capacity refers to the power that the storage device can still charge or discharge at the current time, reflecting the energy that the storage device can currently use for scheduling. The cycle life cost is used to evaluate the impact of scheduling operation on the life of the storage device. According to the second metric, the remaining chargeable capacity and the cycle life cost are measured, forming the second schedulable capacity. Assuming that the initial capacity of the storage device is C0, the current remaining capacity is C r , the cycle life number is N, the used cycle number is n, and the unit cycle life cost is C c , the remaining chargeable and dischargeable capacity can be expressed as C r , and the cycle life cost can be quantified as the cost related to the used cycle number. Here, for the sake of simplifying the calculation, it is assumed that the impact of each scheduling operation on the cycle life is the same, and the scheduling power is P and the scheduling time is t. The increase of the cycle life cost per scheduling operation can be approximately expressed as wherein Ec is the energy change amount corresponding to each cycle. By comprehensively considering the remaining chargeable and dischargeable capacity and the cycle life cost, the second schedulable capacity is formed, and on the premise that the cycle life cost does not exceed a certain threshold, the remaining chargeable and dischargeable capacity is taken as the main reference index to determine the power capacity of the storage device that can be used for grid scheduling.

[0033] At the same time, for the photovoltaic device, based on the short-term predicted output curve as the third metric, the power that the photovoltaic power generation device can provide in the future time period is quantified, and the third dispatchable capacity is formed. The output of the photovoltaic device is affected by many factors such as light intensity, weather conditions, etc., and has great uncertainty. The predicted output curve is obtained by using a prediction algorithm such as time series analysis algorithm on the historical power generation data and meteorological data to predict the power generation of the photovoltaic device in the future. For example, obtain meteorological data that has a significant correlation with photovoltaic power generation, including light intensity, temperature, etc., and collect historical power generation data of the photovoltaic device, including actual power generation in different time periods and under different weather conditions. The collected data is preprocessed to remove outliers and noise, ensuring the accuracy and reliability of the data. The time series analysis algorithm is trained through historical meteorological data and corresponding actual power generation, and the current meteorological data of the photovoltaic device is input into the time series analysis algorithm for analysis to predict the trend of photovoltaic power generation in the future. The predicted output curve is obtained. Assuming that the predicted output curve is P pv (t), where t is the time, and the power Q3 that the photovoltaic device can provide in a certain time period t1-t2 can be calculated by integration: The formula quantifies the power that the photovoltaic power generation device can provide in the future time period, forming the third dispatchable capacity.

[0034] Further, the first metric-the first dispatchable capacity, the second metric-the second dispatchable capacity, and the third metric-the third dispatchable capacity are used as quantitative standards to quantify the light storage and charging devices covered by the target power grid area, and the quantification results are unified and arranged and added to the dispatch baseline library. The dispatch baseline library is the basic data source for the agent unit to perform light storage and charging collaborative scheduling, ensuring the scientificity and executability of light storage and charging collaborative optimization scheduling, and improving the safety and reliability of power grid scheduling.

[0035] Further, the local agent cluster is constructed, including: setting the light storage and charging collaborative mode, wherein the light storage and charging collaborative mode is defined as photovoltaic priority local consumption and complementary smoothing of energy storage; obtaining the light storage and charging topology of the target power grid area, and constructing the local agent cluster, wherein the mapping of the light storage and charging topology-feeder attribution-local agent cluster is used as the dispatch optimization architecture, and the same power grid feeder is used as an agent unit, and the dispatch baseline library is used as the basis for constructing the agent unit.

[0036] Specifically, a light storage and charging coordination mode is set, which defines the priority and complementary strategy of various devices, and the light storage and charging coordination mode includes: photovoltaic priority local consumption and energy storage complementarity smoothing, wherein the photovoltaic priority local consumption means that, under the condition of meeting the local load, the photovoltaic power generation device is used as much as possible to directly power the charging device or the energy storage unit, thereby reducing the phenomenon of light abandonment, and the phenomenon of light abandonment means that photovoltaic power generation is forced to stop due to inability to be consumed, causing resource waste. The energy storage device acts as a regulating buffer to realize power smoothing and peak clipping through charging and discharging complementarity, thereby ensuring the stability of the power grid. By defining the light storage and charging coordination mode, not only the scheduling strategy basis is provided, but also the operation logic of local energy management is formed.

[0037] The GIS contains the geographical position information and electrical connection information of the power grid devices, and the distribution and connection mode of the light storage and charging devices in the power grid are directly presented through a visual interface. The geographical information system (GIS) of the power grid is used to obtain the light storage and charging topology of the target power grid region, which is the spatial distribution and mutual connection relationship of various photovoltaic, energy storage and charging nodes in the power grid. Through the light storage and charging topology information and the feeder attribution, a local agent cluster is constructed, wherein the light storage and charging nodes in each feeder are mapped to the corresponding agent unit to form a mapping relationship of the light storage and charging topology-feeder attribution-local agent cluster, and the mapping relationship is used as the scheduling optimization architecture. The same power grid feeder is taken as an agent unit, so that each agent unit can better adapt to the operation demand of the feeder, thereby improving the pertinence and effectiveness of the scheduling. The scheduling baseline library is used as the basis for constructing the agent unit, and the scheduling baseline library stores the quantified results of the schedulable capacity of the charging device, the energy storage device and the photovoltaic device under the unified dimensioning, including the first scheduling capacity, the second scheduling capacity and the third scheduling capacity. In the construction of the agent unit, the schedulable characteristics of each device in each agent unit can be determined according to the quantified data in the scheduling baseline library, so as to reasonably configure resources, determine the scheduling strategy and operation parameters of the agent unit, and improve the energy utilization efficiency.

[0038] By defining the light storage and charging coordination mode, the self-consumption proportion of photovoltaic power is improved, and the fluctuation of the power grid is reduced. By obtaining the light storage and charging topology, the light storage and charging nodes are corresponded to the power grid feeders one by one to construct a local agent cluster, so as to ensure the matching of the agent scheduling and the physical topology of the power grid, realize the fine management and scheduling of the light storage and charging devices in the target power grid region, improve the reliability and effectiveness of the light storage and charging coordination optimization, improve the energy utilization efficiency, and reduce the operation cost.

[0039] The power grid data is collected, gain processing is performed according to the PGA module, and double-channel transmission of ADC and high-speed comparator and power grid coordination control are performed. The high-speed comparator performs dynamic setting of the high-speed comparator threshold value based on the power data to protect and drive the fluctuation of the power grid.

[0040] Specifically, through the sensor module arranged at each light storage and charging node, such as voltage sensor, current sensor and other key operating parameters of power grid voltage, current, power and the like, real-time collection of power grid data is carried out. The collected power grid data is transmitted to the PGA programming gain amplifier module for gain processing. The PGA module automatically adjusts the amplification multiple according to the strength of the input power grid data signal, dynamically adjusts the amplitude of the sensing signal, ensures that the power grid data is in the best signal-to-noise ratio interval before analog-to-digital conversion, and improves the accuracy and reliability of the power grid data. For example, when the voltage signal of a certain node in the power grid is weak, the PGA module increases the gain to improve the availability of the power grid data.

[0041] The power grid data after gain processing will be transmitted to the ADC (analog-to-digital converter) and high-speed comparator at the same time to form a dual-channel transmission analog-to-digital. Among them, the ADC is used to convert the gained analog power grid data into digital signal, and the high-speed comparator is used for real-time monitoring of power data. Through the execution of high-speed comparator threshold dynamic setting based on power data, the input signal and the set threshold value are quickly compared, and the corresponding level signal is output according to the comparison result. The high-speed comparator is an electronic element that can quickly compare the size of two signals and output the comparison result. The power state of the power grid fluctuates constantly with factors such as load change, power generation situation, etc. The threshold value of the comparator is dynamically set according to the real-time power data of the power grid. For example, when the load of the power grid is light, the set power threshold can be relatively low. In the peak load period, in order to improve the stability of the power grid, the power threshold is correspondingly increased. By dynamically setting the threshold value, the high-speed comparator can monitor whether the power of the power grid exceeds the normal range in real time. When an abnormal power is detected, the protection drive of the power grid fluctuation is carried out, for example, when an emergency occurs in the power grid, the protection device such as circuit breaker tripping is triggered immediately to ensure the fast response ability and protection reliability of the power grid fluctuation, and thus to ensure the safe operation of the power grid. Based on the digital signal converted by the ADC and the protection drive signal output by the high-speed comparator, the coordination control of the power grid is carried out to realize the comprehensive monitoring and effective protection of the power grid, improve the stability and reliability of the power grid, and ensure the safe and efficient operation of the power grid.

[0042] The ADC converts the power grid data into digital data, and the upper dispatching analysis of the minimalist controller in the power grid control center and the light storage and charging coordination of the local intelligent agent cluster are coordinated to manage the power grid light storage and charging.

[0043] Specifically, the analog grid signal processed by the PGA module gain is converted into digital data by the ADC, which is used to convert analog signals such as voltage, current, power, etc. into discrete digital signals. The converted digital data is input to the minimal controller in the grid control center, which is the upper dispatch analysis unit, used for real-time monitoring and analysis of the energy state of the device nodes in the target grid area. In the minimal controller, the digital data is analyzed by the upper dispatch analysis, which refers to the evaluation and judgment of the overall operation state of the grid based on the preset rules combined with the real-time operation data of the grid. At the same time, through the matching of digital data in the local agent group, according to the upper dispatch analysis and the light storage charging coordination of the local agent group, executable light storage charging action instructions are generated, and conflict judgment and elimination are performed when necessary, realizing the coordinated management of grid light storage charging.

[0044] Through the upper dispatch analysis of the minimal controller combined with the light storage charging coordination of the local agent group, the precise coordination management of grid light storage charging scheduling is realized, improving the real-time and accuracy of grid scheduling, and effectively improving the stability and reliability of grid operation.

[0045] Further, before gain processing by the PGA module, including: for the light storage charging topology, integrating a dedicated PGA channel in each charging node, energy storage node and photovoltaic node; for each dedicated PGA channel, the first thread connects the ADC, and the second thread connects the high-speed comparator.

[0046] Specifically, before PGA module gain processing, node-level signal channel arrangement is performed on the light storage charging topology in the target grid area. For each light storage charging topology node, including charging nodes, energy storage nodes and photovoltaic nodes, a dedicated PGA channel is integrated in each node. The dedicated PGA channel is used to amplify or attenuate the analog grid signal collected by the node, and each node corresponds to an independent channel. By integrating a dedicated PGA channel in each node independently, local grid signal processing and high-precision control are realized, ensuring the independence and reliability of the grid data of each node.

[0047] After the channel integration is completed, a double-thread connection mechanism is configured for each dedicated PGA channel: the first thread connects the ADC, which is used to convert the analog signal processed by the PGA gain into a digital signal, and the second thread connects the high-speed comparator, which is used to monitor the grid data in real time and execute protection actions through dynamic threshold judgment. Among them, the first thread and the second thread are not the traditional operating system level strictly defined thread concept with fixed running mechanism and resource allocation rules, but the visualization description of the two different data transmission and processing paths connected by the dedicated PGA channel from the data flow direction and function realization angle.

[0048] The integration of the dedicated PGA channel in each node of the optical storage and charging device can ensure independent data collection and processing of different nodes, improve the accuracy and reliability of the power grid data, and avoid power grid data errors caused by node interference. The dedicated PGA channel is connected to the ADC and the high-speed comparator, forming a double-thread connection mechanism, realizing parallel processing of data collection and protection functions, and obtaining high-precision, real-time power grid digital signals, and enabling the protection mechanism to quickly respond without affecting scheduling calculations when the power grid fluctuates, ensuring the safe and stable operation of the optical storage and charging scheduling.

[0049] Further, the gain processing according to the PGA module includes: the sensing module collects power grid data and transfers to the dedicated PGA channel for gain processing to determine the gain power grid data; wherein the gain processing step includes: transmitting the power grid data of the first node to the corresponding first dedicated PGA channel, if the sensing signal strength is less than the preset standard interval, performing gain up; if the sensing signal strength is greater than the preset standard interval, performing gain down.

[0050] Specifically, the PGA channel is introduced at the output end of the photovoltaic panel, the charging and discharging circuit of the energy storage battery, and the input end of the charging pile, etc. The sensing module distributed in the photovoltaic, energy storage and charging nodes collects power grid data in real time, including voltage, current, etc. At the same time of data collection, the collected power grid data is transferred to the dedicated PGA channel inside the node, and the PGA module adjusts the signal amplitude adaptively to generate high-precision gain power grid data, ensuring the high fidelity of the power grid data. During the gain adjustment process, according to the current working conditions, such as light intensity, battery SOC, charging power, and dynamic parameters such as power grid port current and voltage changes, the gain value of the PGA is automatically adjusted to realize adaptive enhancement of the power grid signal, keep the power grid signal within the best dynamic range of the ADC input end, and avoid signal loss or signal saturation distortion. For example, the gain is increased to capture weak current when photovoltaic light is weak, the gain is reduced to prevent saturation distortion when light is strong, the gain is increased to accurately measure when the energy storage small current charges and discharges, and the gain is reduced to ensure linearity when high power is through.

[0051] The specific steps of gain processing include: comparing the signal strength of each node in real time, when the sensing signal is lower than the lower limit of the preset standard interval, performing gain up, and when the signal strength is higher than the upper limit of the standard interval, performing gain down. For example, in order to determine whether the collected signal strength is in a reasonable range, based on the node operating characteristics, equipment range and historical working condition statistical data, the standard interval is set, such as taking the mean value ±20% of the node in the normal operating state as the upper and lower limits of the standard interval. The power grid data of the first node is transmitted to the corresponding first special PGA channel, and the sensing signal strength is monitored in real time, which refers to the amplitude of the analog signal collected by the sensor, reflecting the actual situation of the power grid parameters. And compare the sensing signal with the preset standard interval in real time, when the sensing signal strength is less than the lower limit of the standard interval, it means that the signal amplitude is small, and the gain up operation is performed, and by adjusting the gain coefficient of the PGA channel, the weak signal is amplified to the appropriate amplitude range. For example, the standard interval is 2-8V, and the current collected voltage signal is 1V, which is less than the lower limit of the standard interval, and the PGA channel increases the gain coefficient, so that the signal amplitude is increased to the standard interval. When the sensing signal strength is greater than the upper limit of the standard interval, it means that the signal amplitude is too large, which may cause saturation, distortion and other problems in the circuit, affecting the accuracy and reliability of the data, and the gain down operation is performed to realize dynamic gain matching and ensure that the signal remains high-fidelity characteristics under different working conditions. Among them, the PGA channel has a multi-stage programmable gain structure, such as ×1, ×2, ×4, ×8 optional.

[0052] Through accurate gain processing, it is ensured that the collected power grid data is always in a suitable amplitude range, improving the usability and accuracy of the data, and further ensuring the effectiveness and pertinence of the light storage and charging coordination scheduling optimization strategy, and ensuring the stability and safety of the power grid operation.

[0053] Further, the dual-channel transmission of ADC and high-speed comparator is coordinated and controlled with the power grid, including: dual-channel synchronous transmission of the gain power grid data, the ADC receives the gain power grid data and writes into the minimalist controller of the power grid central control; the high-speed comparator receives the power data in the gain power grid data, and dynamically sets the threshold value of the high-speed comparator according to the power level.

[0054] Specifically, a dual-channel synchronous transmission mechanism is adopted, which simultaneously transmits the gain grid data to two independent signal paths. In the first path, the ADC receives the gain grid data and converts the analog signal into a digital signal, and writes the converted data into the grid control center's minimal controller in real time. The minimal controller of the grid control center is the core interface of the upper scheduling layer, which is used for summarizing the operation status of the light storage and charging node and issuing strategy tasks. In the second path, the high-speed comparator compares the power data in the received gain data with the preset power threshold. The high-speed comparator is internally provided with a dynamic threshold setting mechanism, which automatically adjusts the comparison threshold according to the real-time power level. The power level refers to the standard interval divided according to the rated power and actual operating power range of the grid equipment, and different power levels correspond to different operating states and safety requirements. For example, when the grid is in a high-power mode, the preset power threshold is correspondingly increased to avoid false alarms triggered by transient spikes, and in a low-power precision mode, the preset power threshold is appropriately reduced to improve the sensitivity of overcurrent or fluctuation detection. Through the dynamic adjustment strategy, adaptive protection response and grid coordination control are realized under different operating conditions of the grid.

[0055] Through dual-channel synchronous transmission, comprehensive processing of grid data is realized. The ADC converts analog data into digital data, providing accurate data basis for the minimal controller of the grid control center, so that the minimal controller can intelligently regulate and control the grid based on accurate digital data, realize load distribution, voltage regulation and other optimization operations, and improve the operating efficiency of the grid. The high-speed comparator monitors the power data in real time, realizes rapid detection of abnormal changes in grid power, and timely triggers protection mechanisms such as cutting off faulty node equipment and adjusting power generation, to prevent equipment damage and grid accidents caused by abnormal power, and to ensure the safe and stable operation of the grid. Through dual-channel synchronous transmission and coordinated control, the reliability and stability of grid operation management are improved.

[0056] Further, the high-speed comparator threshold is dynamically set according to the power level, including: the power level is positively linearly related to the high-speed comparator threshold; if it is a high-power mode, a high overcurrent protection threshold is used to dynamically set the high-speed comparator associated with the grid sensing position; if it is a low-power precision mode, a low overcurrent protection threshold is used to dynamically set the high-speed comparator associated with the grid sensing position.

[0057] Specifically, the high-speed comparator is the trigger prior unit of the power grid protection circuit, which determines whether to trigger the protection action of abnormal fluctuation by comparing with the real-time power signal, and the threshold setting reflects the sensitivity and effectiveness of the protection action. The node power data is obtained through ADC and PGA gain, and the power level is calculated in real time according to the operation conditions of each node, such as photovoltaic output, energy storage charging and discharging state, charging load level, etc. The power level is linearly positively correlated with the threshold of the high-speed comparator, that is, the higher the power level, the larger the corresponding comparator trigger threshold, so as to avoid false triggering caused by transient fluctuation in high power mode. The lower the power level, the lower the high-speed comparator threshold, and the higher the protection sensitivity.

[0058] When it is judged that the node is in high power mode, for example, the photovoltaic output is close to the rated power or the energy storage is in high power discharge, the threshold of the high-speed comparator corresponding to the node is automatically adjusted to a higher value. At this time, the high-speed comparator only triggers the protection action when there is a significant power mutation or serious overcurrent, so as to ensure the stability and anti-interference performance in high power operation. When the node is in low power precision mode, for example, photovoltaic low-illumination power generation, energy storage small-current regulation or charging pile trickle charging stage, the threshold is adjusted to a lower level. The low overcurrent protection threshold is dynamically set for the high-speed comparator associated with the power grid sensing position, so that the high-speed comparator can quickly identify small abnormalities such as arc discharge, transient micro-short circuit and small amplitude overcurrent fluctuation, and ensure the safety response under the micro-disturbance of the power grid.

[0059] According to the real-time power characteristics of the power grid, the high-speed comparator is dynamically set to realize accurate protection under different operating conditions of the power grid, avoid false alarm of normal fluctuation in high power mode, ensure rapid detection of micro-abnormalities in low power mode, and improve the safety and stability of photovoltaic energy storage and charging coordination optimization.

[0060] Further, after the ADC receives the gain grid data and writes it into the minimalist controller of the grid control center, the method comprises: generating a multi-threaded upper scheduling signal according to the minimalist controller of the grid control center and broadcasting it to the target grid area, wherein the upper scheduling signal at least includes a high-energy factor signal and an energy storage factor signal, the high-energy factor signal is a discharge incentive, and the energy storage factor signal is a charging incentive; the local intelligent agent cluster receives the directional thread upper scheduling signal, each intelligent agent unit executes heuristic photovoltaic energy storage and charging coordination in the feeder range, and generates photovoltaic energy storage and charging scheduling instructions.

[0061] Specifically, the minimal controller of the power grid control center receives voltage, current and power data from multiple agent nodes, uses the built-in multi-thread parallel computing structure to divide the target power grid into multiple computing threads, each thread corresponding to a feeder range. Each thread independently analyzes the energy state of the nodes within its management range, calculates the total power by subtracting the load and charging power from photovoltaic power generation and energy storage discharge. If the total power is greater than 0, it indicates that the current thread area has excess energy, which needs to be balanced through energy storage charging or reducing photovoltaic grid connection. If the total power is less than 0, it indicates that the power is insufficient, and there is an energy gap. By identifying the energy surplus and gap in the current power grid area, a multi-threaded upper dispatch signal is generated. Multi-threading refers to the differentiated processing of different power grid partitions by the minimal controller. The upper dispatch signal includes at least a high-energy factor signal and an energy storage factor signal. The high-energy factor signal is a discharge incentive signal used to guide devices with discharge capability. For example, during peak electricity consumption periods, when the power supply capacity of the power grid is insufficient, the high-energy factor signal triggers the energy storage battery to release stored energy to the power grid to alleviate the power supply pressure and ensure the stable operation of the power grid. The energy storage factor signal is a charging incentive that mainly indicates devices that need to be charged, such as charging devices or energy storage devices that perform charging operations at appropriate time periods. For example, during off-peak electricity consumption periods, the power supply of the power grid is relatively abundant, and the energy storage factor signal instructs energy storage devices or charging devices to perform charging operations to absorb excess energy or reduce peak power, balancing the load of the power grid. The minimal controller generates independent dispatch threads for different feeders and different agent units through the multi-thread mechanism, realizes target power grid area broadcasting, and ensures accurate communication and effective execution of control instructions.

[0062] The local agent cluster is the execution unit of power grid regulation and control, which receives the upper dispatch signal of the directional thread in real time. The receiving method of the directional thread ensures that each agent unit can accurately receive the dispatch information related to itself, avoiding signal interference or misoperation. Based on the received upper dispatch signal, multiple agent units in the local agent cluster perform heuristic coordination of the feeder range, where heuristic refers to a decision-making method based on experience rules and local information, which can quickly and flexibly respond to the power grid environment. For example, the agent unit makes decisions based on the following rules: photovoltaic power is preferentially consumed locally to reduce power grid feeder fluctuations; energy storage devices are used to smooth power fluctuations or discharge under high-energy factor signals, charge under low-energy factor signals, and charging devices can adjust power according to flexible charging windows to participate in load regulation. The feeder range refers to the use of heuristic algorithms to cooperatively schedule photovoltaic, energy storage and charging devices within the feeder range managed by each agent unit, and to generate executable photovoltaic, energy storage and charging scheduling instructions.

[0063] The intelligent agent unit generates reliable and executable photovoltaic- energy storage-charging coordination scheduling instructions in the feeder range according to the upper signal and device capability, which not only ensures the balance and stability of the power grid, but also realizes the efficient utilization of photovoltaic- energy storage-charging resources.

[0064] Further, each intelligent agent unit performs heuristic photovoltaic- energy storage-charging coordination in the feeder range to generate photovoltaic- energy storage-charging scheduling instructions, including: the first intelligent agent unit receives a first upper scheduling signal, matches photovoltaic- energy storage-charging nodes in the first feeder in a scheduling baseline library, each photovoltaic- energy storage-charging node performs heuristic scheduling decision to determine photovoltaic- energy storage-charging action instructions, and performs conflict judgment and resolution on the photovoltaic- energy storage-charging action instructions to generate first photovoltaic- energy storage-charging scheduling instructions, wherein the first photovoltaic- energy storage-charging scheduling instructions perform photovoltaic- energy storage-charging coordination scheduling in the first feeder.

[0065] Specifically, after the upper controller generates and broadcasts the upper scheduling information, each intelligent agent unit receives a directional scheduling signal corresponding to the feeder to which it belongs and performs heuristic photovoltaic- energy storage-charging coordination in the feeder range. Taking the first intelligent agent unit as an example, after the first intelligent agent unit receives the first upper scheduling signal of the first feeder, it performs scheduling decision on photovoltaic nodes, energy storage nodes and charging nodes in its management range. By matching the schedulable capacity of each node in the scheduling baseline library, including parameters such as charging and discharging power range, remaining capacity and cycle life of energy storage battery, and photovoltaic predicted output curve, the scheduling operation space that each node can participate in is determined.

[0066] By matching in the scheduling baseline library, the first intelligent agent unit performs photovoltaic- energy storage-charging scheduling decision according to heuristic rules, such as preferentially local consumption of power generation, smoothing of power fluctuations by using energy storage, and adjustment of charging power according to charging time window, etc. Each node generates a preliminary operation scheme as a photovoltaic- energy storage-charging action instruction. Since different node actions may conflict at the same time, the intelligent agent unit performs conflict judgment and resolution on the photovoltaic- energy storage-charging action instructions, coordinates the node actions, eliminates the operation contradictions in time and space, and at the same time ensures that the overall scheduling target is not affected. For example, energy storage charging and photovoltaic full output at the same time may cause the feeder voltage to exceed the limit, at this time, a peak-shaving scheduling sequence is generated to delay the energy storage charging slightly, which not only eliminates the conflict, but also does not affect the overall target.

[0067] After conflict processing, the first intelligent agent unit generates the final first photovoltaic- energy storage-charging scheduling instructions, which contain specific operation instructions and priorities of each node and can be directly issued to the execution device to realize the coordinated scheduling of photovoltaic- energy storage-charging devices in the first feeder.

[0068] By converting higher-level dispatch signals into executable action commands, specific control of photovoltaic-storage-charging nodes is achieved, enabling coordinated operation of photovoltaic, energy storage, and charging equipment within the feeder range. This facilitates photovoltaic power generation absorption, smooth energy storage regulation, and rational load allocation. Furthermore, conflict determination and resolution mechanisms enhance the safety and stability of photovoltaic-storage-charging dispatch, preventing power over-limits or voltage fluctuations. Through heuristic feeder-range photovoltaic-storage-charging coordination executed by each intelligent agent unit, dispatch commands are generated, enabling unified control and optimization of photovoltaic, energy storage, and charging equipment in the target grid area. This improves the real-time performance, accuracy, and reliability of grid dispatch, ensuring energy balance and stable supply under different operating conditions.

[0069] Example 2, based on the same inventive concept as the intelligent scheduling-based optical storage-charging coordination optimization method in the foregoing examples, such as... Figure 2 As shown, this application provides a photovoltaic-storage-charging coordination optimization system based on intelligent scheduling, wherein the photovoltaic-storage-charging coordination optimization system based on intelligent scheduling includes:

[0070] The intelligent agent cluster construction module 11 is used to construct a local intelligent agent cluster for a target power grid area by grouping photovoltaic, energy storage, and charging nodes based on feeder affiliation and building intelligent agent units, wherein each power grid feeder corresponds to one intelligent agent unit; the task execution module 12 is used to collect power grid data, perform gain processing according to the PGA module, and execute dual-path transmission of ADC and high-speed comparator and power grid coordination and control; wherein the high-speed comparator performs dynamic setting of high-speed comparator threshold based on power data to drive protection against power grid fluctuations; the coordination management module 13 is used to convert power grid data into digital data by ADC, and perform grid photovoltaic, energy storage, and charging coordination management with the upper-level scheduling analysis of the simplified controller in the power grid central control and the local intelligent agent cluster.

[0071] Furthermore, the intelligent agent cluster construction module 11 is also used to: quantify the dispatchable capabilities of charging equipment, energy storage equipment, and photovoltaic equipment under a unified metric; wherein, the quantification method includes: for charging equipment, using battery status, flexible charging time window, and power range as the first metric; for energy storage equipment, using remaining charging and discharging capacity and cycle life cost as the second metric; for photovoltaic equipment, using predicted output curve as the third metric; quantifying the photovoltaic, energy storage, and charging equipment covered by the target power grid area using the first metric - first dispatchable capability, the second metric - second dispatchable capability, and the third metric - third dispatchable capability, and adding them to the dispatch baseline library.

[0072] Further, the intelligent agent cluster construction module 11 is further configured to: set a light-storage-charging coordination mode, wherein the light-storage-charging coordination mode is defined by local consumption of photovoltaic power in priority and complementary smoothing by energy storage; and obtain a light-storage-charging topology of a target power grid region, and construct a local intelligent agent cluster, wherein a mapping of the light-storage-charging topology-feeder line attribution-local intelligent agent cluster is taken as a scheduling optimization architecture, a same power grid feeder line is taken as an intelligent agent unit, and a scheduling baseline library is taken as a construction basis of the intelligent agent unit.

[0073] Further, the task execution module 12 is further configured to: integrate a dedicated PGA channel in each charging node, energy storage node and photovoltaic node one by one for the light-storage-charging topology; and connect an ADC by a first thread and a high-speed comparator by a second thread for each dedicated PGA channel.

[0074] Further, the task execution module 12 is further configured to: perform power grid data collection by a sensing module, and transfer the power grid data to the dedicated PGA channel for gain processing to determine gain power grid data; wherein the gain processing step includes: transmitting power grid data of a first node to a corresponding first dedicated PGA channel, and performing gain up-regulation if a sensing signal strength is less than a preset standard interval, or performing gain down-regulation if the sensing signal strength is greater than the preset standard interval.

[0075] Further, the task execution module 12 is further configured to: perform double-path synchronous transmission on the gain power grid data, the ADC receives the gain power grid data and writes the gain power grid data into a minimalist controller of a power grid central control, and the high-speed comparator receives power data in the gain power grid data and dynamically sets a high-speed comparator threshold value according to a power level.

[0076] Further, the task execution module 12 is further configured to: the power level and the high-speed comparator threshold value are in linear positive correlation; if it is a high-power mode, a high overcurrent protection threshold value is used to dynamically set the high-speed comparator associated with a power grid sensing position; and if it is a low-power precision mode, a low overcurrent protection threshold value is used to dynamically set the high-speed comparator associated with the power grid sensing position.

[0077] Further, the coordination management module 13 is further configured to: generate a multi-threaded upper-level scheduling signal according to the minimalist controller of the power grid central control, and broadcast the upper-level scheduling signal in the target power grid region, wherein the upper-level scheduling signal at least includes a high-energy factor signal and an energy storage factor signal, the high-energy factor signal is a discharge incentive, and the energy storage factor signal is a charging incentive; the local intelligent agent cluster receives the upper-level scheduling signal of the directional thread, each intelligent agent unit performs heuristic light-storage-charging coordination of a feeder line range, and generates a light-storage-charging scheduling instruction.

[0078] Further, the cooperation management module 13 is further configured to: the first intelligent agent unit receives a first upper dispatch signal, and performs matching on the optical storage and charging nodes in the first feeder line in a dispatch baseline library, each optical storage and charging node executes a heuristic dispatch decision to determine an optical storage and charging action instruction; and performs conflict judgment and resolution on the optical storage and charging action instruction to generate a first optical storage and charging dispatch instruction, wherein the first optical storage and charging dispatch instruction performs optical storage and charging cooperation dispatch in the first feeder line.

[0079] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The intelligent scheduling based optical storage and charging cooperation optimization system in the present embodiment is also applicable to the intelligent scheduling based optical storage and charging cooperation optimization method and specific examples in the first embodiment. Those skilled in the art can clearly understand the intelligent scheduling based optical storage and charging cooperation optimization system in the present embodiment according to the detailed description of the intelligent scheduling based optical storage and charging cooperation optimization method. In order to make the specification concise, the intelligent scheduling based optical storage and charging cooperation optimization system will not be described in detail.

[0080] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0081] Obviously, for those skilled in the art, some improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims

1. A method for optimizing cooperation of optical storage and charging based on intelligent scheduling, characterized in that, The method comprises: For the target power grid area, a local intelligent agent cluster is constructed by performing light storage and charging node grouping and agent unit building based on feeder attribution, wherein each power grid feeder corresponds to an agent unit; Collecting power grid data, performing gain processing according to the PGA module, and performing dual-channel transmission of ADC and high-speed comparator and power grid coordination control; Wherein, the high-speed comparator performs dynamic setting of the high-speed comparator threshold value based on power data to protect and drive the power grid fluctuation; The ADC converts the power grid data into digital data, and the local intelligent agent cluster performs light storage and charging coordination based on the upper scheduling analysis of the minimalist controller in the power grid central control to manage the light storage and charging of the power grid; Wherein, the gain processing according to the PGA module comprises: The sensing module collects power grid data and transfers it to a special PGA channel for gain processing to determine gain power grid data; Wherein, the gain processing step comprises: Transmit the power grid data of the first node to the corresponding first special PGA channel, if the sensing signal strength is less than the preset standard interval, perform gain up; If the sensing signal strength is greater than the preset standard interval, perform gain down; Wherein, performing dual-channel transmission of ADC and high-speed comparator and power grid coordination control comprises: Dual-channel synchronous transmission of the gain power grid data, the ADC receives the gain power grid data and writes it into the minimalist controller of the power grid central control; The high-speed comparator receives the power data in the gain power grid data and dynamically sets the high-speed comparator threshold value according to the power level; Wherein, after the ADC receives the gain power grid data and writes it into the minimalist controller of the power grid central control, it comprises: According to the minimalist controller of the power grid central control, generate a multi-threaded upper scheduling signal and broadcast it to the target power grid area, wherein the upper scheduling signal at least contains a high-energy factor signal and a storage factor signal, the high-energy factor signal is a discharge incentive, and the storage factor signal is a charging incentive; The local intelligent agent cluster receives the directional thread upper scheduling signal, each agent unit performs heuristic light storage and charging coordination of the feeder range, and generates a light storage and charging scheduling instruction. 2.The intelligent dispatching based optical storage and charging cooperation optimization method according to claim 1, characterized in that, Before the agent unit is built, it comprises: Quantifying the schedulable capacity of the charging equipment, energy storage equipment and photovoltaic equipment under unified measurement; Wherein, the quantification method comprises: For the charging equipment, the battery state, flexible charging time window and power range are used as the first measurement standard; For the energy storage equipment, the residual charging and discharging capacity and the cycle life cost are used as the second measurement standard; For the photovoltaic equipment, the predicted output curve is used as the third measurement standard; The first measurement standard-first schedulable capacity, the second measurement standard-second schedulable capacity, and the third measurement standard-third schedulable capacity are used to quantify the light storage and charging equipment covering the target power grid area and add them to the scheduling baseline library. 3.The intelligent dispatching based optical storage and charging coordination optimization method of claim 2, wherein, Constructing a local intelligent agent cluster comprises: Setting a light storage and charging coordination mode, wherein the photovoltaic priority local consumption and the complementary smoothing of the energy storage define the light storage and charging coordination mode; An optical storage charging topology of a target power grid area is acquired, and a local agent cluster is constructed, wherein a mapping of the optical storage charging topology-feeder line attribution-local agent cluster is taken as a scheduling optimization architecture, the same power grid feeder line is taken as an agent unit, and a scheduling baseline library is taken as a construction basis for the agent unit. 4.The intelligent dispatching based optical storage and charging coordination optimization method of claim 3, wherein, Before gain processing is performed according to the PGA module, the following is included: For the optical storage charging topology, a dedicated PGA channel is integrated in each charging node, energy storage node and photovoltaic node one by one; For each dedicated PGA channel, a first thread is connected to an ADC, and a second thread is connected to a high-speed comparator. 5.The intelligent dispatching based optical storage and charging coordination optimization method of claim 1, wherein, According to the power level, the threshold value of the high-speed comparator is dynamically set, including: wherein the power level is positively correlated with the threshold value of the high-speed comparator in a linear manner; if it is a high-power mode, a high overcurrent protection threshold value is used to dynamically set the high-speed comparator associated with the power grid sensing position; if it is a low-power precision mode, a low overcurrent protection threshold value is used to dynamically set the high-speed comparator associated with the power grid sensing position. 6.The intelligent dispatching based optical storage and charging coordination optimization method of claim 5, wherein, Each agent unit performs heuristic coordination of the feeder line range of the optical storage charging, and generates an optical storage charging scheduling instruction, including: The first agent unit receives a first upper scheduling signal, matches the optical storage charging nodes in the first feeder line in the scheduling baseline library, each optical storage charging node performs heuristic scheduling decision, and determines an optical storage charging action instruction; The optical storage charging action instruction is subjected to conflict judgment and resolution, and a first optical storage charging scheduling instruction is generated, wherein the first optical storage charging scheduling instruction performs optical storage charging coordination scheduling in the first feeder line.

7. The intelligent scheduling-based light storage and charging cooperation optimization system, characterized in that, Steps for implementing the optical storage charging coordination optimization method based on intelligent scheduling according to any one of claims 1 to 6, including: An agent cluster construction module is used to construct a local agent cluster by grouping the optical storage charging nodes under the attribution of the feeder line and building an agent unit for the target power grid area, wherein each power grid feeder line corresponds to an agent unit; A task execution module is used to collect power grid data, perform gain processing according to the PGA module, and perform double-channel transmission of the ADC and the high-speed comparator and power grid coordination control; wherein the high-speed comparator performs dynamic setting of the high-speed comparator threshold value based on power data, and performs protection driving of power grid fluctuations; A coordination management module is used to convert the power grid data into digital data by the ADC, perform upper scheduling analysis by the minimalist controller in the power grid central control, and perform optical storage charging coordination by the local agent cluster, and perform optical storage charging coordination management of the power grid.

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