Optical storage and charging cooperation optimization method and system based on intelligent scheduling

Abstract

The invention provides an optical storage and charging cooperation optimization method and system based on intelligent scheduling, and relates to the technical field of energy scheduling, and the method comprises the steps: forming a local intelligent agent cluster for a target power grid region, collecting power grid data, carrying out gain processing according to a PGA module, and executing the dual-path transmission of an ADC and a high-speed comparator and power grid coordination control. The high-speed comparator executes high-speed comparator threshold value dynamic setting based on power data, and protection driving of power grid fluctuation is carried out; the ADC converts power grid data into digital data, and power grid optical storage and charging cooperation management is carried out through upper scheduling analysis of an extremely simple controller in a power grid center controller and optical storage and charging coordination of a local agent cluster. According to the invention, the technical problems that the real-time response is slow and the prediction error affects the scheduling precision and reliability in the optical storage and charging scheduling method in the prior art are solved, and the technical effects of coordinating and optimizing the optical storage and charging equipment and improving the real-time performance, precision and reliability of power grid scheduling can be achieved.

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 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 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 according to a PGA module, execute dual-channel transmission of ADC and high-speed comparator, and coordinate and control the grid; 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 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 the 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: The method provided by the embodiments of the present application constructs 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; collects grid data, performs gain processing according to a PGA module, executes dual-channel transmission of ADC and high-speed comparator, and coordinates and controls the grid; 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 converts the grid data into digital data by the ADC, 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 the 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.

[0017] 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

[0018] 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.

[0019] Figure 1 This is a flowchart illustrating the optimization method for the optical storage-charging coordination based on intelligent scheduling provided in this application.

[0020] Figure 2 This is a schematic diagram of the structure of the intelligent scheduling-based photovoltaic-storage-charging optimization system provided in this application.

[0021] Figure labeling: Intelligent agent cluster construction module 11, task execution module 12, cooperation management module 13. Detailed Implementation

[0022] 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.

[0023] 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.

[0024] 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: For the target power grid area, a local intelligent agent cluster is formed by grouping photovoltaic, energy storage and charging nodes and building intelligent agent units based on feeder affiliation, in which each power grid feeder corresponds to one intelligent agent unit.

[0025] Specifically, within the target power grid area, the covered photovoltaic-storage-charging (PV-SGC) devices are grouped according to their grid feeder affiliation. These devices include photovoltaic power generation equipment, energy storage equipment, and charging equipment. A feeder refers to a distribution line extending from a substation in the power grid, with each feeder covering the load and distributed energy resources of a specific area. The topology information of the target power grid area is obtained, the feeder to which each PV-SGC node belongs is identified, and the nodes are classified according to their type and function. For example, PV nodes focus on output prediction, energy storage nodes focus on remaining capacity and cycle life, and charging nodes focus on flexible charging time and power range.

[0026] After identifying the feeder affiliation, an intelligent agent unit is created for each grid feeder. This intelligent agent unit is the smallest control unit for local scheduling, managing the photovoltaic, energy storage, and charging equipment within the corresponding feeder, enabling local decision-making and collaborative control. During the construction of the intelligent agent unit, the schedulable capability information of each photovoltaic, energy storage, and charging node is loaded into the corresponding intelligent agent unit, including indicators such as predicted photovoltaic output, available energy storage capacity, and charging flexibility. Based on the grouping of photovoltaic, energy storage, and charging nodes under feeder affiliation and the construction of intelligent agent units, a local intelligent agent cluster is formed with feeders as units. Each intelligent agent unit can independently execute photovoltaic, energy storage, and charging scheduling, or achieve multi-level collaboration.

[0027] Furthermore, before the intelligent agent unit is built, the following steps are taken: quantifying the dispatchable capabilities of charging equipment, energy storage equipment, and photovoltaic equipment under a unified metric. The quantification methods include: 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; and for photovoltaic equipment, using the predicted output curve as the third metric. The photovoltaic, energy storage, and charging equipment covered by the target power grid area are quantified using the first metric - first dispatchable capability, the second metric - second dispatchable capability, and the third metric - third dispatchable capability, and then added to the dispatch baseline library.

[0028] Specifically, between the construction of intelligent agent units, the dispatchable capacity of photovoltaic, energy storage and charging equipment, namely charging equipment, energy storage equipment and photovoltaic equipment in the target power grid area is quantified under a unified metric. The dispatchable capacity refers to the power capacity or energy contribution capacity of each device that can be used for grid dispatch within a certain time range.

[0029] The specific quantification methods for dispatchable capability include: quantification of charging equipment, such as charging piles or charging units, using battery status, flexible charging time windows, and allowable power range as the primary metric. Battery status, reflected by State of Charge (SOC), indicates the current remaining battery capacity and determines the amount of electrical energy the charging equipment can receive or provide. For example, when a battery is severely aged, its acceptable charging power decreases, and its discharge capacity weakens. The flexible time window refers to the dispatchable start and end time period for charging; the acceptable charging power may differ in different time periods. For example, during periods of low grid load, the charging equipment can charge at a higher power. The power range refers to the upper and lower limits of the charging rate under normal operating conditions. By combining battery status, flexible charging time windows, and allowable power range, the maximum amount of electricity the charging equipment can provide or absorb in the current time period can be calculated, forming the primary dispatchable capability, reflecting the degree to which the charging equipment can participate in grid dispatch under the current state. This primary dispatchable capability... Where Q1 represents the maximum range of electrical energy that the charging device can provide or absorb during the current time period, t start and t end These are the start and end times of the flexible charging time window, respectively. and These represent the lower and upper limits of the charging device's power when the battery is in SOC state and time is t, respectively.

[0030] For energy storage devices, the remaining chargeable / dischargeable capacity and cycle life cost are used as the second metric. The remaining chargeable / dischargeable capacity refers to the amount of electricity the energy storage device can still charge or discharge at the current moment, reflecting the energy currently available for dispatch. Cycle life cost is used to assess the impact of dispatch operations on the lifespan of the energy storage device. The remaining charging capacity and cycle life cost are measured according to the second metric to form the second dispatchable capacity. Assume the initial capacity of the energy storage device is C0, and the current remaining capacity is C... r The cycle life is N, the number of cycles used is n, and the cost per unit cycle life is C. c The remaining charge / discharge capacity can then be expressed as C. r Cycle lifetime cost can be quantified as the cost associated with the number of cycles used. For simplicity, we assume that each scheduling operation has the same impact on cycle lifetime, and that the scheduling power is P and the scheduling time is t. Then, the increase in cycle lifetime cost for each scheduling operation can be approximated as: Where Ec represents the energy change corresponding to each cycle. Combining the remaining charge / discharge capacity and cycle life cost, a second dispatchable capacity is formed. Under the premise of ensuring that the cycle life cost does not exceed a certain threshold, the remaining charge / discharge capacity is used as the main reference indicator to determine the power capacity of the energy storage device that can be used for grid dispatch.

[0031] Meanwhile, for photovoltaic (PV) equipment, a third metric is used—the short-term predicted output curve—to quantify the electrical energy that PV power generation equipment can provide over a future period, thus forming a third dispatch capability. The output of PV equipment is affected by various factors such as sunlight intensity and weather conditions, exhibiting significant uncertainty. The predicted output curve is obtained by using meteorological data, historical power generation data, and prediction algorithms, such as time series analysis algorithms, to predict the power generation of PV equipment over a future period. For example, meteorological data significantly correlated with PV power generation, including sunlight intensity and temperature, is obtained, along with historical power generation data of the PV equipment, including actual power generation under different time periods and weather conditions. The collected data is preprocessed to remove outliers and noise, ensuring data accuracy and reliability. The time series analysis algorithm is trained using historical meteorological data and corresponding actual power generation. The current meteorological data of the PV equipment is input into the time series analysis algorithm for analysis to predict the trend of PV power generation over a future period, obtaining the predicted output curve. Let the predicted output curve be represented as P... pv (t), where t is time. Within a certain time period t1-t2, the electrical energy Q3 that the photovoltaic equipment can provide can be calculated by integration: This formula quantifies the electrical energy that photovoltaic power generation equipment can provide in the future time period, forming a third dispatchable capability.

[0032] Furthermore, the first metric standard—first dispatchable capability, the second metric standard—second dispatchable capability, and the third metric standard—third dispatchable capability are used as quantification standards to quantify the photovoltaic, energy storage, and charging equipment covered by the target power grid area. The quantification results are then uniformly organized and added to the scheduling baseline library. The scheduling baseline library is the basic data source for the intelligent agent unit to perform photovoltaic, energy storage, and charging collaborative scheduling, ensuring the scientific nature and feasibility of photovoltaic, energy storage, and charging collaborative optimization scheduling, and improving the safety and reliability of power grid scheduling.

[0033] Furthermore, the formation of a local intelligent agent cluster includes: setting a photovoltaic-storage-charging collaborative mode, wherein photovoltaic power is prioritized for local consumption and energy storage is used for complementary and smoothing definition of the photovoltaic-storage-charging collaborative mode; obtaining the photovoltaic-storage-charging topology of the target power grid area, and constructing a local intelligent agent cluster, wherein the mapping of photovoltaic-storage-charging topology-feeder affiliation-local intelligent agent cluster is used as the scheduling optimization architecture, the same power grid feeder is used as an intelligent agent unit, and the scheduling baseline library is used as the basis for constructing intelligent agent units.

[0034] Specifically, a photovoltaic-storage-charging (PV-SGC) collaborative mode is established. This mode clarifies the priorities and complementary strategies of various devices. The mode includes prioritizing local photovoltaic power consumption, with energy storage providing complementary smoothing. Prioritizing local photovoltaic power consumption means, while meeting local load requirements, utilizing photovoltaic power generation equipment to directly supply power to charging equipment or energy storage units as much as possible, thereby reducing curtailment. Curtailment refers to the forced shutdown of photovoltaic power generation due to insufficient power consumption, resulting in resource waste. Energy storage devices act as a regulating buffer, achieving power smoothing and peak shaving through complementary charging and discharging, ensuring grid stability. Defining the PV-SGC collaborative mode not only provides a foundation for scheduling strategies but also establishes the operational logic for local energy management.

[0035] GIS contains geographic location information and electrical connection information of power grid equipment, and visually presents the distribution and connection methods of photovoltaic, energy storage, and charging devices in the power grid through a visual interface. Using the power grid's GIS, the photovoltaic, energy storage, and charging topology of the target power grid area is obtained. This topology represents the spatial distribution of each photovoltaic, energy storage, and charging node in the power grid and their interconnections. Based on the photovoltaic, energy storage, and charging topology information and feeder affiliation, a local intelligent agent cluster is constructed. Each photovoltaic, energy storage, and charging node within a feeder is mapped to a corresponding intelligent agent unit, forming a mapping relationship of photovoltaic, energy storage, and charging topology - feeder affiliation - local intelligent agent cluster. This mapping relationship serves as the scheduling optimization architecture. Treating the same power grid feeder as an intelligent agent unit allows each intelligent agent unit to better adapt to the operational needs of its feeder, improving the targeting and effectiveness of scheduling. A scheduling baseline library is used as the basis for constructing intelligent agent units. This library stores the results of quantifying the schedulable capabilities of charging equipment, energy storage equipment, and photovoltaic equipment under a unified metric, including first scheduling capability, second scheduling capability, and third scheduling capability. When constructing intelligent agent units, the schedulable characteristics of each device within each intelligent agent unit can be clearly defined based on the quantitative data in the scheduling baseline library. This allows for the rational allocation of resources, determination of the scheduling strategy and operating parameters of the intelligent agent unit, and improvement of energy utilization efficiency.

[0036] By defining a photovoltaic-storage-charging (PV-SGC) collaborative mode, the self-consumption ratio of photovoltaic power is increased, and grid fluctuations are reduced. By acquiring the PV-SGC topology, each PV-SGC-charging node is mapped one-to-one with the grid feeder, and a local intelligent agent cluster is constructed. This ensures that the intelligent agent scheduling matches the physical topology of the grid, enabling refined management and scheduling of PV-SGC-charging equipment within the target grid area. This improves the reliability and effectiveness of PV-SGC-charging coordination optimization, increases energy utilization efficiency, and reduces operating costs.

[0037] The system collects power grid data, performs gain processing based on the PGA module, and executes dual-path transmission via ADC and high-speed comparator for coordinated power grid management. Specifically, the high-speed comparator dynamically sets its threshold based on power data to drive protection against power grid fluctuations.

[0038] Specifically, grid data is acquired in real time by sensor modules deployed at each photovoltaic-storage-charging node, such as voltage and current sensors, which collect key operating parameters like grid voltage, current, and power. The acquired grid data is then transmitted to a PGA (Programmable Gain Amplifier) ​​module for gain processing. The PGA module automatically adjusts the amplification factor based on the strength of the input grid data signal, dynamically adjusting the amplitude of the sensor signal to ensure the grid data is in the optimal signal-to-noise ratio range before analog-to-digital conversion, thus improving the accuracy and reliability of the grid data. For example, when the voltage signal at a node in the grid is weak, the PGA module increases the gain, improving the availability of the grid data.

[0039] After gain processing, the grid data is simultaneously transmitted to an ADC (Analog-to-Digital Converter) and a high-speed comparator, forming a dual-channel analog-to-digital transmission. The ADC converts the amplified analog grid data into a digital signal, while the high-speed comparator monitors power data in real time. By dynamically setting the comparator threshold based on power data, it quickly compares the input signal with the set threshold and outputs a corresponding level signal based on the comparison result. The high-speed comparator is an electronic component capable of quickly comparing the magnitudes of two signals and outputting the comparison result. The power state of the grid fluctuates continuously with load changes, power generation, and other factors. The comparator threshold is dynamically set based on the real-time power data of the grid. For example, when the grid load is light, the set power threshold can be relatively low; during peak load periods, the power threshold is increased accordingly to improve grid stability. By dynamically setting the threshold, the high-speed comparator can monitor whether the grid power exceeds the normal range in real time. When an abnormal power is detected, it triggers protection mechanisms to mitigate grid fluctuations. For example, in the event of an emergency in the grid, it immediately triggers protection devices, such as circuit breaker tripping, ensuring rapid response and protection reliability during grid fluctuations, thereby guaranteeing the safe operation of the grid. Based on the digital signal converted by ADC and the protection drive signal output by the high-speed comparator, the power grid is coordinated and managed to achieve 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.

[0040] The ADC converts grid data into digital data, and coordinates the grid's photovoltaic, energy storage, and charging with the upper-level scheduling analysis of the simplified controller in the grid's central control system and the local intelligent agent cluster.

[0041] Specifically, the analog grid signal, after being processed by the PGA module gain, is converted into digital data by an ADC. The ADC is used to convert analog signals such as voltage, current, and power into discrete digital signals. The converted digital data is input to a simplified controller within the grid central control unit. This simplified controller is a higher-level scheduling and analysis unit used to monitor and analyze the energy status of equipment nodes in the target grid area in real time. Within the simplified controller, the digital data undergoes higher-level scheduling analysis. This analysis assesses and determines the overall operating status of the grid based on preset rules and real-time grid operation data. Simultaneously, the digital data is matched with the local intelligent agent cluster. Based on the coordination between the higher-level scheduling analysis and the local intelligent agent cluster's photovoltaic, energy storage, and charging capabilities, executable photovoltaic, energy storage, and charging action commands are generated. Conflict determination and resolution are performed when necessary, achieving coordinated management of photovoltaic, energy storage, and charging within the grid.

[0042] By combining the upper-level scheduling analysis of the simplified controller with the coordination of photovoltaic, energy storage, and charging by the local intelligent agent cluster, precise coordination management of photovoltaic, energy storage, and charging scheduling in the power grid is achieved, improving the real-time performance and accuracy of power grid scheduling, and effectively enhancing the stability and reliability of power grid operation.

[0043] Furthermore, before performing gain processing on the PGA module, the process includes: for the photovoltaic-storage-charging topology, integrating dedicated PGA channels 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.

[0044] Specifically, before performing gain processing on the PGA module, node-level signal channels are arranged for the photovoltaic-storage-charging topology within the target power grid area. For each node in the photovoltaic-storage-charging topology, including charging nodes, energy storage nodes, and photovoltaic nodes, a dedicated PGA channel is integrated into each node. The dedicated PGA channel is used to amplify or attenuate the analog power grid signals acquired by the node, with each node corresponding to an independent channel. By independently integrating a dedicated PGA channel at each node, localization and high-precision control of power grid signal processing are achieved, ensuring the independence and reliability of power grid data at each node.

[0045] After channel integration is completed, a dual-thread connection mechanism is configured for each dedicated PGA channel: the first thread connects to the ADC to convert the analog signal after PGA gain processing into a digital signal, and the second thread connects to a high-speed comparator to monitor power grid data in real time and execute protection actions based on dynamic threshold judgments. The first and second threads are not strictly defined threads with fixed operating mechanisms and resource allocation rules at the traditional operating system level, but rather a figurative representation of the two different data transmission and processing paths connected to the dedicated PGA channel from the perspective of data flow and functional implementation.

[0046] Integrating dedicated PGA channels within each node of the photovoltaic-storage-charging system ensures independent data acquisition and processing at different nodes, improving the accuracy and reliability of grid data and avoiding grid data errors caused by inter-node interference. Connecting the dedicated PGA channels to both an ADC and a high-speed comparator forms a dual-thread connection mechanism, enabling parallel processing of data acquisition and protection functions. This provides high-precision, real-time grid digital signals and allows the protection mechanism to respond quickly to grid fluctuations without affecting scheduling calculations, ensuring the safe and stable operation of the photovoltaic-storage-charging system.

[0047] Furthermore, gain processing is performed based on the PGA module, including: the sensor module collects grid data, transfers it to a dedicated PGA channel for gain processing, and determines the gain grid data; wherein, the gain processing steps include: transmitting the grid data of the first node to the corresponding first dedicated PGA channel; if the sensor signal strength is less than a preset standard range, the gain is increased; if the sensor signal strength is greater than the preset standard range, the gain is decreased.

[0048] Specifically, PGA channels are introduced at key nodes such as the photovoltaic panel output, the energy storage battery charging and discharging circuit, and the charging pile input. Real-time grid data, including voltage and current at the nodes, is collected by sensor modules distributed across the photovoltaic, energy storage, and charging nodes. Simultaneously, the collected grid data is transferred to a dedicated PGA channel within the node. The PGA module adaptively adjusts the signal amplitude gain to generate high-precision gain grid data, ensuring high fidelity. During gain adjustment, the PGA gain value is automatically adjusted based on current operating conditions, such as light intensity, battery SOC, charging power, and dynamic parameters like grid port current and voltage changes. This achieves adaptive enhancement of the grid signal, keeping it within the optimal dynamic range at the ADC input and preventing signal loss or saturation distortion. For example, the gain is increased during low-light photovoltaic power generation to capture weak currents, decreased during strong light to prevent saturation distortion, increased during low-current charging and discharging of energy storage for accurate measurement, and decreased during high-power throughput to ensure linearity.

[0049] The specific steps of gain processing include: real-time comparison of the signal strength of each node; when the sensor signal is below the lower limit of the preset standard range, gain is increased; when the signal strength is above the upper limit of the standard range, gain is decreased. For example, to determine whether the acquired signal strength is within a reasonable range, a standard range is set based on node operating characteristics, equipment range, and historical operating condition statistics. For instance, the average signal value of the node under normal operating conditions ±20% is used as the upper and lower limits of the standard range. The grid data of the first node is transmitted to the corresponding first dedicated PGA channel to monitor the sensor signal strength in real time. The sensor signal strength refers to the amplitude of the analog signal acquired by the sensor, reflecting the actual situation of the grid parameters. The sensor signal is compared with the preset standard range in real time. When the sensor signal strength is less than the lower limit of the standard range, it indicates that the signal amplitude is small, and gain is increased. By adjusting the gain coefficient of the PGA channel, the weak signal is amplified to a suitable amplitude range. For example, if the standard range is 2-8V and the currently acquired voltage signal is 1V, which is less than the lower limit of the standard range, the PGA channel increases the gain coefficient to raise the signal amplitude to the standard range. When the sensor signal strength exceeds the upper limit of the standard range, it indicates that the signal amplitude is too large, which may lead to circuit saturation, distortion, and other problems, affecting data accuracy and reliability. In response, a gain reduction operation is performed to achieve dynamic gain matching, ensuring that the signal maintains high fidelity characteristics under different operating conditions. The PGA channel features a multi-level programmable gain structure, such as ×1, ×2, ×4, and ×8 selectable.

[0050] By precisely processing the gain, we ensure that the collected power grid data is always within a suitable amplitude range, thereby improving the availability and accuracy of the data. This, in turn, ensures the effectiveness and relevance of the photovoltaic-storage-charging coordinated scheduling optimization strategy, and safeguards the stability and security of power grid operation.

[0051] Furthermore, the dual-path transmission and grid coordination management of the ADC and high-speed comparator includes: dual-path synchronous transmission of the gain grid data; the ADC receiving the gain grid data and writing it into the simplified controller of the grid central control; the high-speed comparator receiving the power data in the gain grid data and dynamically setting the high-speed comparator threshold according to the power level.

[0052] Specifically, a dual-path synchronous transmission mechanism using an ADC and a high-speed comparator is employed to simultaneously transmit gain grid data to two independent signal paths. In the first path, the ADC receives the gain grid data, converts the analog signal into a digital signal, and writes the converted data into the simplified controller of the grid central control system in real time. This simplified controller serves as the core interface of the upper-level scheduling layer, used for summarizing the operating status of photovoltaic, energy storage, and charging nodes and issuing policy tasks. In the second path, the high-speed comparator compares the power data in the received gain data with a preset power threshold. The high-speed comparator incorporates a dynamic threshold setting mechanism that automatically adjusts the comparison threshold based on the real-time power level. The power level refers to a standard range defined by the rated power and actual operating power range of grid equipment; different power levels correspond to different operating states and safety requirements. For example, when the grid is in high-power mode, the preset power threshold is increased accordingly to avoid false alarms triggered by instantaneous spikes; in low-power precision mode, the preset power threshold is appropriately decreased to improve the sensitivity of overcurrent or fluctuation detection. Through this dynamic adjustment strategy, adaptive protection response and coordinated grid management under different grid operating conditions are achieved.

[0053] Dual-channel synchronous transmission enables comprehensive processing of power grid data. The ADC converts analog data into digital data, providing a precise data foundation for the simplified controller in the power grid central control system. This allows the simplified controller to intelligently regulate the power grid based on accurate digital data, achieving optimized operations such as load distribution and voltage regulation, thereby improving the power grid's operating efficiency. The high-speed comparator monitors power data in real time, enabling rapid detection of abnormal changes in power grid output and timely triggering of protection mechanisms, such as disconnecting faulty equipment and adjusting generation power, to prevent equipment damage and power grid accidents caused by abnormal power output, ensuring the safe and stable operation of the power grid. Dual-channel synchronous transmission and coordinated control improve the reliability and stability of power grid operation and management.

[0054] Furthermore, based on the power level, the high-speed comparator threshold is dynamically set, including: wherein the power level and the high-speed comparator threshold are linearly positively correlated; 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 sensor location; 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 sensor location.

[0055] Specifically, the high-speed comparator serves as the triggering prior unit for the grid protection circuit. By comparing the signal with the real-time power signal, it determines whether to trigger a protection action due to abnormal fluctuations. The threshold setting reflects the sensitivity and effectiveness of the protection action. Node power data is acquired through ADC and PGA gains, and the power level is calculated in real-time based on the operating conditions of each node, such as photovoltaic output, energy storage charging and discharging status, and charging load level. The power level is linearly positively correlated with the high-speed comparator threshold; that is, the higher the power level, the larger the corresponding comparator trigger threshold, thus avoiding false triggering caused by transient fluctuations in high-power mode. Conversely, the lower the power level, the smaller the high-speed comparator threshold, improving protection sensitivity.

[0056] When a node is determined to be in high-power mode, such as when photovoltaic output is close to rated power or energy storage is discharging at high power, the threshold of the corresponding high-speed comparator is automatically increased to a higher value. In this case, the high-speed comparator only triggers dynamic protection when there is a significant power surge or severe overcurrent, ensuring stability and anti-interference under high-power operation. When a node is in low-power precision mode, such as during low-illuminance photovoltaic power generation, low-current regulation of energy storage, or trickle charging of charging piles, the threshold is lowered to a lower level. With a low overcurrent protection threshold, the high-speed comparator associated with the grid sensor location is dynamically set, enabling the high-speed comparator to quickly identify minor anomalies such as arc discharge, transient micro-short circuits, and small overcurrent fluctuations, ensuring a safe response under grid disturbances.

[0057] The height comparator is dynamically set according to the real-time power characteristics of the power grid to achieve precise protection under different power grid operating conditions. In high power mode, it avoids false alarms due to normal fluctuations, and in low power mode, it ensures rapid detection of micro-anomalies, thereby improving the safety and stability of photovoltaic-storage-charging coordinated optimization.

[0058] Furthermore, after the ADC receives the gain grid data and writes it into the simplified controller of the grid central control, it includes: generating a multi-threaded upper-level scheduling signal according to the simplified controller of the grid central control and broadcasting it to the target grid area, wherein the upper-level scheduling signal includes at least a high-energy factor signal and an energy storage factor signal, the high-energy factor signal being a discharge excitation and the energy storage factor signal being a charging excitation; the local intelligent agent cluster receives the upper-level scheduling signal for the directional thread, and each intelligent agent unit performs heuristic feeder-range optical-storage-charging coordination to generate optical-storage-charging scheduling instructions.

[0059] Specifically, the simplified controller of the power grid central control receives voltage, current, and power data from multiple intelligent agent nodes. Utilizing its built-in multi-threaded parallel computing structure, it divides the target power grid into multiple computing threads, each corresponding to a feeder range. Each thread independently analyzes the energy status of nodes within its management range, calculating the total node 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's region has excess power, requiring energy storage charging or reducing photovoltaic grid connection to balance the load. If the total power is less than 0, it indicates insufficient power, with an energy gap. By identifying the current power grid region's energy surplus and gap, it generates multi-threaded upper-level scheduling signals. Multi-threading refers to the simplified controller's differentiated processing of different power grid zones. The upper-level scheduling signals include at least a high-energy factor signal and an energy storage factor signal. The high-energy factor signal is a discharge excitation signal used to guide devices with discharge capabilities. For example, during peak electricity consumption periods, when the power grid's supply capacity is insufficient, the high-energy factor signal triggers the energy storage battery to release stored energy into the grid to alleviate power supply pressure and ensure stable grid operation. The energy storage factor signal serves as a charging excitation, primarily instructing devices requiring charging, such as charging equipment or energy storage devices, to charge during appropriate time periods. For instance, during off-peak hours when the grid's power supply is relatively abundant, the energy storage factor signal instructs energy storage or charging devices to perform charging operations, absorbing excess energy or reducing peak power to balance the grid load. The simplified controller uses a multi-threaded mechanism to generate independent scheduling threads for different feeders and intelligent agent units, enabling broadcasting to the target grid area. The target grid area is a pre-defined specific region based on the grid topology, equipment distribution, and actual operational needs. Unified broadcasting and scheduling of the target grid area ensures accurate transmission and effective execution of control commands.

[0060] The local intelligent agent cluster serves as the execution unit for grid regulation. It receives real-time scheduling signals from the higher-level dispatching thread. This directed-thread reception method ensures that each intelligent agent unit accurately receives scheduling information relevant to itself, avoiding signal interference or misoperation. Multiple intelligent agent units within the local cluster execute heuristic feeder-range photovoltaic-storage-charging coordination based on the received higher-level dispatching signals. Heuristic refers to a decision-making method based on empirical rules and local information, enabling rapid and flexible responses to the grid environment. For example, the intelligent agent units make decisions based on the following rules: photovoltaic power is prioritized for local consumption to reduce grid feeder fluctuations; energy storage devices are used to smooth power fluctuations or discharge under high-energy-factor signals and charge under low-energy-factor signals; charging devices can adjust power according to flexible charging windows to participate in load regulation. The feeder range refers to the area within which each intelligent agent unit manages the feeder. Within this range, the unit uses heuristic algorithms to collaboratively schedule photovoltaic-storage-charging devices and generates executable photovoltaic-storage-charging scheduling instructions.

[0061] Based on upper-level signals and equipment capabilities, the intelligent agent unit can quickly generate reliable and executable photovoltaic-storage-charging coordinated scheduling commands within the feeder range, ensuring both grid balance and stability, and achieving efficient utilization of photovoltaic-storage-charging resources.

[0062] Furthermore, each intelligent agent unit performs heuristic-based optical storage and charging coordination within the feeder range, generating optical storage and charging scheduling instructions. This includes: the first intelligent agent unit receiving a first upper-level scheduling signal, matching the optical storage and charging nodes in the first feeder in the scheduling baseline library, each optical storage and charging node performing heuristic scheduling decisions to determine optical storage and charging action instructions; and performing conflict determination and resolution on the optical storage and charging action instructions to generate a first optical storage and charging scheduling instruction, wherein the first optical storage and charging scheduling instruction executes optical storage and charging coordination scheduling in the first feeder.

[0063] Specifically, after the upper-level controller generates and broadcasts the upper-level scheduling information, each intelligent agent unit receives the directional scheduling signal corresponding to its feeder affiliation and performs heuristic feeder-range photovoltaic-storage-charging coordination. Taking the first intelligent agent unit as an example, after receiving the first upper-level scheduling signal for the first feeder, the first intelligent agent unit makes scheduling decisions on the photovoltaic nodes, energy storage nodes, and charging nodes within its management range. By matching the schedulable capabilities of each node in the scheduling baseline library, including parameters such as charging and discharging power range, remaining capacity and cycle life of the energy storage battery, and photovoltaic predicted output curve, the scheduling operation space in which each node can participate is determined.

[0064] By matching against the scheduling baseline library, the first intelligent agent unit makes photovoltaic-storage-charging scheduling decisions according to heuristic rules, such as prioritizing local power generation consumption, utilizing energy storage to smooth power fluctuations, and adjusting charging power according to charging time windows. Each node generates a preliminary operation plan as photovoltaic-storage-charging action instructions. Since the actions of different nodes may conflict at the same time, the intelligent agent unit performs conflict judgment and resolution on the photovoltaic-storage-charging action instructions. Through peak-shifting scheduling, power allocation optimization, or time series adjustment, it coordinates the actions of each node, eliminates spatiotemporal operational contradictions, and ensures that the overall scheduling objective is not affected. For example, if energy storage charging and photovoltaic full-capacity output occur simultaneously, it may cause the feeder voltage to exceed the limit. In this case, a peak-shifting scheduling sequence is generated to slightly delay energy storage charging, which resolves the conflict without affecting the overall objective.

[0065] After conflict resolution, the first intelligent agent unit generates the final first optical storage and charging scheduling instruction. The first optical storage and charging scheduling instruction contains the specific operation instructions and priorities of each node and can be directly issued to the execution device to realize the coordinated scheduling of optical storage and charging devices within the first feeder.

[0066] 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.

[0067] 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: 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.

[0068] 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.

[0069] Furthermore, the intelligent agent cluster construction module 11 is also used to: set a photovoltaic-storage-charging collaborative mode, wherein the photovoltaic is prioritized for local consumption and energy storage is used for complementary and smooth definition of the photovoltaic-storage-charging collaborative mode; obtain the photovoltaic-storage-charging topology of the target power grid area, and construct a local intelligent agent cluster, wherein the mapping of photovoltaic-storage-charging topology-feeder affiliation-local intelligent agent cluster is used as the scheduling optimization architecture, the same power grid feeder is used as an intelligent agent unit, and the scheduling baseline library is used as the basis for constructing intelligent agent units.

[0070] Furthermore, the task execution module 12 is also used to: integrate dedicated PGA channels in each charging node, energy storage node, and photovoltaic node for the photovoltaic-storage-charging topology; for each dedicated PGA channel, the first thread is connected to the ADC, and the second thread is connected to the high-speed comparator.

[0071] Furthermore, the task execution module 12 is also used for: the sensing module to collect power grid data, transfer it to a dedicated PGA channel for gain processing, and 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 a preset standard range, performing gain adjustment; if the sensing signal strength is greater than a preset standard range, performing gain adjustment.

[0072] Furthermore, the task execution module 12 is also used for: performing dual-channel synchronous transmission of the gain grid data; the ADC receiving the gain grid data and writing it into the simplified controller of the grid central control; and the high-speed comparator receiving the power data in the gain grid data and dynamically setting the high-speed comparator threshold according to the power level.

[0073] Furthermore, the task execution module 12 is also used to: have a linear positive correlation between the power level and the high-speed comparator threshold; if it is a high-power mode, dynamically set the high-speed comparator associated with the grid sensor location with a high overcurrent protection threshold; if it is a low-power precision mode, dynamically set the high-speed comparator associated with the grid sensor location with a low overcurrent protection threshold.

[0074] Furthermore, the coordination management module 13 is also used to: generate multi-threaded upper-level scheduling signals and broadcast them to the target power grid area according to the simplified controller of the power grid central control, wherein the upper-level scheduling signals include at least a high-energy factor signal and an energy storage factor signal, the high-energy factor signal being a discharge excitation and the energy storage factor signal being a charging excitation; the local intelligent agent cluster receives the upper-level scheduling signals of the directional threads, and each intelligent agent unit performs heuristic feeder-range optical-storage-charging coordination to generate optical-storage-charging scheduling instructions.

[0075] Furthermore, the coordination management module 13 is also used for: the first intelligent agent unit receiving the first upper-level scheduling signal, matching the optical storage and charging nodes in the first feeder in the scheduling baseline library, each optical storage and charging node performing heuristic scheduling decision to determine the optical storage and charging action command; performing conflict judgment and resolution on the optical storage and charging action command to generate a first optical storage and charging scheduling command, wherein the first optical storage and charging scheduling command executes the optical storage and charging coordination scheduling in the first feeder.

[0076] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The optical storage and charging coordination optimization method and specific examples based on intelligent scheduling in the aforementioned embodiment 1 are also applicable to the optical storage and charging coordination optimization system based on intelligent scheduling in this embodiment. Through the foregoing detailed description of the optical storage and charging coordination optimization method based on intelligent scheduling, those skilled in the art can clearly understand the optical storage and charging coordination optimization system based on intelligent scheduling in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0077] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0078] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this 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 agent cluster is constructed by performing agent unit construction based on light storage charging node grouping under feeder attribution; 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; Among them, the high-speed comparator performs dynamic setting of the high-speed comparator threshold based on the power data to protect and drive the power grid fluctuation; The ADC converts the power grid data into digital data, and the local agent cluster cooperates with the light storage charging to manage the power grid. 2.The intelligent dispatching based optical storage and charging coordination optimization method of claim 1, wherein, Before the agent unit is built, it includes: Quantifying the schedulable capacity of charging equipment, energy storage equipment and photovoltaic equipment under unified measurement; Among them, the quantification method includes: For charging equipment, the battery state, flexible charging time window and power range are used as the first measurement standard; For energy storage equipment, the residual charging and discharging capacity and the cycle life cost are used as the second measurement standard; For 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 charging equipment covered by 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 agent cluster includes: Setting a light storage charging cooperation mode, wherein the light storage charging cooperation mode is defined as photovoltaic priority local consumption and energy storage complementary smoothing; Obtain the light storage charging topology of the target power grid area, and construct the local agent cluster, wherein the mapping of the light storage charging topology-feeder attribution-local agent cluster is used as the scheduling optimization architecture, and 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. 4.The intelligent dispatching based optical storage and charging coordination optimization method of claim 3, wherein, Before gain processing according to the PGA module, it includes: For the light storage charging topology, integrate a dedicated PGA channel in each charging node, energy storage node and photovoltaic node; For each dedicated PGA channel, the first thread is connected to the ADC, and the second thread is connected to the high-speed comparator. 5.The intelligent dispatching based optical storage and charging coordination optimization method of claim 4, wherein, According to the PGA module, the gain processing includes: The sensing module collects power grid data and transfers it to the dedicated PGA channel for gain processing to determine the gain power grid data; The gain processing steps include: Transmit the power grid data of the first node to the corresponding first dedicated PGA channel, and 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. 6.The intelligent dispatching based optical storage and charging coordination optimization method of claim 5, wherein, Performing dual-channel transmission of ADC and high-speed comparator and power grid coordination control includes: 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 according to the power level. 7.The intelligent dispatching based optical storage and charging coordination optimization method of claim 6, wherein, According to the power level, the high-speed comparator threshold is dynamically set, It includes: Among them, the power level and the high-speed comparator threshold are positively correlated in a linear manner; If it is a high-power mode, a high over-current protection threshold is used to dynamically set a high-speed comparator associated with a power grid sensing position. If it is a low-power precision mode, a low over-current protection threshold is used to dynamically set a high-speed comparator associated with a power grid sensing position. 8.The intelligent dispatching based optical storage and charging coordination optimization method of claim 6, wherein, After the ADC receives the gain grid data and writes into the minimal controller of the grid control center, the following steps are included: 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 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 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. 9.The intelligent dispatching based optical storage and charging coordination optimization method of claim 8, wherein, Each intelligent agent unit executes heuristic light storage and charging coordination of the feeder range, and generates a light storage and charging scheduling instruction, including: 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, and determines a light storage and charging action instruction; 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.

10. An intelligent dispatching-based light storage-charging coordination optimization system, characterized in that, Steps for implementing the intelligent scheduling-based light storage and charging coordination optimization method of any one of claims 1-9, including: An intelligent agent cluster construction module is used to construct a local intelligent agent cluster by grouping light storage and charging nodes under the feeder attribution and building intelligent agent units for the target grid area, wherein each grid feeder corresponds to an intelligent agent unit; A task execution module is used to collect grid data, perform gain processing based on the PGA module, and execute dual-channel transmission of the ADC and the high-speed comparator and grid coordination control; The high-speed comparator performs high-speed comparator threshold dynamic setting based on power data to protect and drive the grid fluctuation. A coordination management module is used for the ADC to convert grid data into digital data, perform upper scheduling analysis by the minimal controller in the grid control center, and perform light storage and charging coordination by the local intelligent agent cluster to manage the grid light storage and charging coordination.

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