A charging station multi-source collaborative control method based on edge computing and related equipment

Abstract

The application belongs to the field of energy management, and provides a charging station multi-source collaborative control method based on edge computing and related equipment, which comprises the following steps: performing fault diagnosis and operation risk prediction according to real-time power quality parameters to obtain fault information and risk early warning information; determining a control target based on the real-time power quality parameters, the fault information and the risk early warning information, and generating a collaborative control strategy by weighting and synthesizing an economy target, a stability target and an environmental protection target in the control target; generating a control instruction to control the collaborative operation of system components, and then collecting real-time power quality parameters of distribution network bus and system components again as executed power quality parameters; calculating the parameter deviation between the executed power quality parameters and the control target, and adjusting the collaborative control strategy and reissuing the control instruction to control the collaborative operation of system components when the parameter deviation exceeds a preset deviation threshold, thereby improving the safety and reliability of system operation.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy management, and specifically relates to a multi-source cooperative control method for charging stations based on edge computing and related devices. Background Art

[0002] The statements in this section merely provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] With the large-scale integration of distributed new energy sources into distribution networks, especially the popularization of high-power and random loads such as electric vehicle charging stations, their inherent intermittency and volatility pose severe challenges to the stable operation of the power grid. Traditional control methods usually rely on the centralized scheduling mode of the cloud master station, which has long communication links and slow decision response, making it difficult to meet the real-time control requirements of the distribution network, and often fails to effectively suppress local disturbances due to delayed control instructions.

[0004] To address the real-time bottleneck of centralized scheduling, the prior art proposes a cooperative control method based on edge computing. In this method, controllers are deployed at the edge nodes of the distribution network close to the data source, and optimization algorithms such as model predictive control are adopted to calculate and issue control instructions according to the real-time state parameters of the power grid. By sinking computing tasks to the network edge, this method significantly shortens the control closed-loop response time, improves the ability to quickly suppress distributed energy fluctuations, and thus alleviates the control delay problem in centralized scheduling.

[0005] However, existing solutions mainly focus on optimizing the operation state of the power grid within the prediction time domain during the optimization process, and abstract each regulation unit in the system into a mathematical model with ideal response characteristics. When performing prospective optimization, this method fails to fully consider the dynamic evolution of the physical state of the equipment itself and its potential health risks. When a certain key equipment (such as the battery module of an energy storage system, a photovoltaic inverter, etc.) has early performance degradation or a hidden fault that has not been detected by the conventional monitoring system, its actual response may deviate from the prediction model, causing the control system to increase the regulation intensity. This process may cause secondary damage to equipment that is already in a sub-health state, accelerate its performance degradation process, and even cause sudden failures, thereby affecting the long-term operation reliability and economy of the system. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a multi-source collaborative control method and related equipment for charging stations based on edge computing. This invention acquires power quality parameters of the distribution network bus and system components in real time for fault diagnosis and operational risk prediction, thereby obtaining comprehensive fault information and risk warnings. Based on this, it dynamically determines multi-dimensional control objectives, including economic, stability, and environmental goals, and generates an adaptive collaborative control strategy. The scheme also introduces a closed-loop feedback mechanism to adjust the strategy based on parameter deviations after control command execution. This invention effectively solves the problems of insufficient real-time performance in traditional solutions and the inability of existing edge control to prevent equipment risks, thereby improving the real-time performance, safety, reliability, economy, and equipment lifespan of the charging station energy system.

[0007] According to some embodiments, the first solution of the present invention provides a multi-source collaborative control method for charging stations based on edge computing, which adopts the following technical solution: A multi-source cooperative control method for charging stations based on edge computing includes: The raw operating data of the distribution network bus and system components are acquired in real time and preprocessed to obtain real-time power quality parameters; Fault diagnosis and operational risk prediction are performed based on real-time power quality parameters to obtain fault information and risk warning information. Control objectives are determined based on real-time power quality parameters, fault information, and risk warning information. These objectives include economic objectives, stability objectives, and environmental objectives. A coordinated control strategy is generated by weighting and integrating the economic, stability, and environmental objectives within the control targets. After the coordinated control strategy generates control commands to control the coordinated operation of system components, the real-time power quality parameters of the distribution network bus and system components are collected again as the power quality parameters after execution. The system calculates the parameter deviation between the power quality parameters and the control target after execution. When the parameter deviation exceeds the preset deviation threshold, the system adjusts the collaborative control strategy and reissues the control command to control the collaborative operation of system components.

[0008] Furthermore, control objectives are determined based on real-time power quality parameters, fault information, and risk warning information. These objectives include economic, stability, and environmental goals, including: Based on the real-time voltage and frequency values ​​in the real-time power quality parameters, the voltage deviation between the real-time voltage value and the voltage reference value, and the frequency deviation between the real-time frequency value and the frequency reference value are calculated respectively; the minimum deviation index formed by the voltage deviation and frequency deviation is taken as the stability target. Based on the real-time electricity price information and real-time power quality parameters of the distribution network, the total electricity cost within a unit dispatch cycle is calculated, with the minimum total electricity cost as the economic objective. Based on the theoretical maximum power generation, the actual power generation of the wind power generation unit, and the actual power generation of the photovoltaic power generation unit, the renewable energy utilization rate is calculated, with the maximum renewable energy utilization rate as the environmental protection target.

[0009] Furthermore, the process of generating a coordinated control strategy by weighting and synthesizing the economic, stability, and environmental objectives within the control objectives includes: Based on fault information and risk warning information, determine the target weight coefficients for economic objectives, stability objectives, and environmental protection objectives; Based on the target weight coefficient, the comprehensive objective function value of the control target is calculated, and the target operation mode of the charging station energy system is identified based on the comprehensive objective function value. The target operation modes include environmental protection priority mode, economic priority mode and stability priority mode. Based on the relationship between the target operating mode and the current operating mode of the charging station's energy system, the final target operating mode of the charging station's energy system is determined. When the final target operating mode is the environmental protection priority mode, a wind and solar power generation priority control strategy is generated. When the final target operating mode is the economy priority mode, an energy storage economic dispatch control strategy is generated. When the final target operating mode is stability-first mode, a voltage and frequency stability control strategy is generated.

[0010] Furthermore, based on the relationship between the target operating mode and the current operating mode of the charging station's energy system, the final target operating mode of the charging station's energy system is determined, including: When the target operating mode is different from the current operating mode of the charging station's energy system, the mode switching timer is started, and the target operating mode is recorded as the operating mode to be switched to. When the mode switching timer reaches the preset switching duration, real-time fault information and risk warning information are reacquired. Based on the reacquired fault information and risk warning information, the updated comprehensive objective function value is calculated and the updated target operating mode is determined according to the updated comprehensive objective function value. If the target operating mode for updating is the same as the operating mode to be switched, then the current operating mode will be switched to the operating mode to be switched. If the target operating mode to be updated is different from the operating mode to be switched, the current operating mode will remain unchanged, and the duration of the mode switching timer will be reset to zero.

[0011] Furthermore, when the final target operating mode is the environmental protection priority mode, a wind and solar power priority control strategy is generated, including: Based on the rated transmission power of the distribution bus and the real-time load power of the distribution bus in the real-time power quality parameters, calculate the remaining connectable power of the distribution bus. When the sum of the actual power generation of the wind power generation unit and the photovoltaic power generation unit does not exceed the remaining available power, the wind power generation unit and the photovoltaic power generation unit are controlled to output in maximum power point tracking mode. When the sum of the actual power generation of the wind power generation unit and the photovoltaic power generation unit exceeds the remaining available power, and the state of charge of the energy storage unit is lower than the upper limit threshold for charging, the electrical energy exceeding the remaining available power will be transferred to the energy storage unit for storage. When the sum of the actual power generation of the wind power generation unit and the photovoltaic power generation unit exceeds the remaining available power, and the state of charge of the energy storage unit is greater than or equal to the charging upper limit threshold, the output power of the wind power generation unit and the photovoltaic power generation unit is limited to the remaining available power.

[0012] Furthermore, when the final target operating mode is the economy-first mode, an energy storage economic dispatch control strategy is generated, including: Based on real-time electricity price information from the distribution network side and preset electricity price thresholds, high-price and low-price periods are identified; During periods of low electricity prices and when the state of charge of the energy storage unit is below the charging target threshold, the energy storage unit is controlled to charge at the rated charging power. During periods of high electricity prices and when the state of charge of the energy storage unit is higher than the lower discharge threshold, the energy storage unit is controlled to discharge at the rated discharge power.

[0013] Furthermore, when the final target operating mode is stability-first mode, a voltage and frequency stability control strategy is generated, including: Obtain the charging demand information of each charging terminal currently connected to the charging unit. The charging demand information includes the charging terminal identifier, current state of charge, and planned charging end time. Based on charging demand information, the charging priority is calculated for each charging terminal. The charging priority is negatively correlated with the current state of charge and negatively correlated with the time difference between the planned end of charging and the current time. Calculate the currently available charging power based on the remaining available power of the wind power generation unit and the photovoltaic power generation unit and the dischargeable power of the energy storage unit; Based on the charging priority from high to low, charging power is allocated to each charging terminal in sequence until the currently available charging power is allocated or the charging needs of all charging terminals are met.

[0014] According to some embodiments, the second aspect of the present invention provides a charging station energy system, which adopts the following technical solution: A charging station energy system includes an edge computing server, a distribution network bus, and system components, wherein the system components include at least a wind power generation unit, a photovoltaic power generation unit, an energy storage unit, and a charging unit; The edge computing server specifically includes: one or more processors and a memory; the memory is used to store computer programs, and when the computer programs are executed by the processors, they implement the multi-source collaborative control method for charging stations based on edge computing described in the first scheme above.

[0015] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the edge computing-based multi-source collaborative control method for charging stations as described in the first embodiment above.

[0017] According to some embodiments, a fourth aspect of the present invention provides a computer program product or computer program.

[0018] A computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the edge computing-based multi-source collaborative control method for charging stations as described in the first embodiment above.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention employs real-time acquisition of power quality parameters of the distribution network bus and system components. Based on these parameters, fault diagnosis and operational risk prediction are performed. Control objectives are determined based on power quality parameters, fault information, and risk warning information. A collaborative control strategy for system components is generated according to these objectives. After the control command is executed, power quality parameters of the distribution network bus and system components are collected again, and parameter deviations are calculated. When the parameter deviation exceeds a preset deviation threshold, the collaborative control strategy is adjusted, and the control command issuance step is re-executed. Therefore, the edge side can perceive the operating status, equipment health, and risks in real time, dynamically determine multiple objectives, and adaptively adjust the strategy in a closed loop. This effectively solves the problems of poor real-time performance of cloud scheduling and insufficient consideration of equipment health risks in related technologies, leading to strategy misadjustment and equipment damage. Ultimately, this improves the real-time performance, safety, reliability, and economy of the charging station energy system and extends equipment lifespan.

[0020] This invention uses fault information and risk warning information to determine the target weight coefficients of economic, stability, and environmental protection objectives. Based on these target weight coefficients, it calculates the comprehensive objective function value of the control objective, identifies the target operation mode of the charging station energy system, and generates corresponding wind and solar power priority control strategies, energy storage economic dispatch control strategies, and voltage and frequency stability control strategies. Therefore, the system can dynamically adjust the optimization focus according to the actual health and risk conditions and intelligently switch strategies to adapt to different scenarios. This effectively solves the limitations of single optimization objectives or fixed weight mechanisms in complex environments in related technologies, thereby improving the robustness, intelligence level, and operating efficiency of the charging station energy system and avoiding the limitations of single optimization objectives.

[0021] This invention employs a mode switching timer. Timing begins when the target operating mode differs from the current operating mode of the charging station's energy system. After a preset switching duration, fault information and risk warning information are reacquired to confirm and update the target operating mode. Switching only occurs when the updated target operating mode matches the operating mode to be switched; otherwise, the current operating mode remains unchanged. This effectively avoids frequent mode switching caused by momentary disturbances, ensuring that switching is based on a stable and verified system condition. It effectively solves the problem in related technologies where control strategies frequently jitter in high-dynamic environments, affecting system stability and equipment lifespan. This, in turn, improves the stability, reliability, and safety of the control strategy, thereby enhancing system stability. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 This is a flowchart of a multi-source collaborative control method for charging stations based on edge computing, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the physical device structure of an edge server in an embodiment of the present invention. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0026] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0027] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0028] Example 1 In related technologies, controllers deployed at edge nodes on the distribution network side can utilize optimization algorithms such as model predictive control to rapidly suppress distributed energy fluctuations and address centralized scheduling delays. The following describes a scenario using a multi-source collaborative control method for charging stations based on edge computing. In this scenario, the control system primarily focuses on optimizing the grid's operating state within the prediction time domain. Typically, each control unit within the system (such as energy storage batteries and photovoltaic inverters) is abstracted into a mathematical model with ideal response characteristics. For example, when voltage fluctuations occur in the grid, the system calculates and issues commands requiring the energy storage unit to perform high-power charging and discharging to smooth the fluctuations. However, this method often fails to fully consider the dynamic evolution of the equipment's physical state during optimization. If the energy storage battery is already in a state of latent aging or sub-health undetected by routine monitoring, and the control system still forcibly schedules according to the ideal model, it can easily cause secondary damage to the equipment, accelerate performance degradation, or even induce failures.

[0029] The closed-loop adaptive control mechanism combining fault diagnosis and operational risk prediction in this embodiment of the invention achieves dynamic weighting and strategy coordination of multi-dimensional control objectives (economic efficiency, stability, and environmental friendliness) by acquiring power quality parameters in real time and integrating equipment health status and fault risk assessment. This not only meets the real-time dispatching needs of the power grid but also ensures the safe operation of system components. The following describes a scenario using a multi-source collaborative control method for charging stations based on edge computing, as described in this invention. In this scenario, the edge computing server not only acquires parameters such as voltage and frequency during real-time monitoring but also diagnoses a high operational risk warning for a certain system component (such as an energy storage unit). Based on this, the system automatically adjusts the control objective weights, shifting from economic priority to stability priority, and generates corresponding voltage and frequency stabilization control strategies. When smoothing grid fluctuations, the system intelligently limits the output of the high-risk energy storage unit and instead achieves the control objective by flexibly scheduling the charging power of charging piles or adjusting the output of wind and solar power generation units. Furthermore, after the command is executed, the system collects parameters again to calculate the deviation. If the deviation is too large, the strategy is readjusted to ensure grid stability without sacrificing equipment lifespan.

[0030] It is evident that the edge computing-based multi-source collaborative control method for charging stations in this embodiment of the invention can achieve efficient and real-time collaborative control of the charging station energy system. It can also effectively solve the problem of equipment damage caused by neglecting equipment health risks in related technologies and the limitation that a single control target cannot adapt to complex working conditions. In this way, it achieves a comprehensive improvement in the safety, reliability, economy and environmental protection of the charging station energy system.

[0031] To facilitate understanding, the method provided in this embodiment will be described in process below, taking into account the above scenario. For example... Figure 1 As shown, this embodiment provides a multi-source collaborative control method for charging stations based on edge computing, applied to the edge computing server of the charging station energy system. The charging station energy system includes a distribution network bus and system components. The system components include at least a wind power generation unit, a photovoltaic power generation unit, an energy storage unit, and a charging unit. In this embodiment, the method includes the following steps: Step S1: Acquire the raw operating data of the distribution network bus and system components in real time and preprocess them to obtain real-time power quality parameters; In this context, the distribution busbar represents the main power transmission line connecting various system components in the charging station's energy system. It serves as the hub for power collection and distribution, typically the backbone of a three-phase AC power system. System components represent the functional units within the charging station's energy system, including at least wind power generation units, photovoltaic power generation units, energy storage units, and charging units. Each unit has specific functions and operating characteristics. Real-time power quality parameters represent key indicators describing the operating status of the power system, including at least real-time voltage, real-time current, real-time frequency, and real-time power, used to assess the quality of power transmission and usage. Real-time power includes the real-time load power of the distribution busbar, the real-time exchange power between the charging station and the distribution network, and the real-time output power of each system component. A positive real-time exchange power value indicates that the charging station purchases power from the distribution network, while a negative value indicates that the charging station outputs power to the distribution network.

[0032] After the charging station's energy system starts up and begins operation, the edge computing server needs to continuously acquire raw operational data. Specifically, the edge computing server collects raw operational data in real time through a sensor network deployed on the distribution network bus and key nodes of various system components. These sensors include voltage transformers (PTs), current transformers (CTs), power analyzers, and frequency measuring devices. The collected raw operational data is transmitted to the edge computing server via fieldbus (such as Modbus RTU / TCP), industrial Ethernet, or wireless communication technologies (such as ZigBee, LoRa, or 5G).

[0033] After receiving the raw operational data, the edge computing server first performs data preprocessing, including noise reduction, filtering, calibration, and timestamp alignment, to ensure data accuracy and consistency. Then, the edge computing server organizes and stores the processed data according to a predetermined data structure, forming a real-time power quality parameter dataset, providing a foundation for subsequent analysis and decision-making.

[0034] The sampling frequency is typically set to multiple times per second (e.g., 10-100 Hz) to capture rapid changes and transients in the power grid. For certain critical parameters, such as voltage sags or frequency fluctuations, a higher sampling rate (e.g., milliseconds) may be required to ensure that no important events are missed.

[0035] Optionally, in some embodiments, the edge computing server integrates with existing smart meters and power quality analyzers in the charging station's energy system to directly read the power quality parameters already measured by these devices. The edge computing server is configured with corresponding communication drivers, supporting standard protocols such as IEC 61850, DNP3, or Modbus, to establish communication connections with the measuring devices. The edge computing server sends data requests to each measuring device according to a preset polling period (e.g., 100 milliseconds), receives the returned parameter values ​​such as voltage, current, frequency, and power, and stores these data in a local time-series database after marking them with timestamps.

[0036] Step S2: Perform fault diagnosis and operational risk prediction based on real-time power quality parameters to obtain fault information and risk warning information; Step S2.1: Perform fault diagnosis based on real-time power quality parameters to obtain fault information, including fault type, fault location, and fault severity; Among them, fault diagnosis refers to the process of identifying and locating faults or abnormal states in the system by analyzing abnormal patterns of power quality parameters. The fault information represents the output of the fault diagnosis, which includes a detailed description of the detected abnormality or fault. The fault type indicates the nature and classification of the fault that occurred, such as overvoltage, undervoltage, overcurrent, short circuit, harmonic exceedance, frequency deviation, etc. The fault location indicates the specific system component or network node where the fault occurred, such as a photovoltaic inverter, a specific energy storage unit, or a section of the distribution network bus. Fault severity indicates an assessment of the scope of impact and potential harm of a fault. It is typically categorized into minor, moderate, and severe levels and is used to guide the prioritization and urgency of subsequent control decisions.

[0037] After acquiring real-time power quality parameters, the edge computing server needs to immediately analyze this data to detect potential faults or anomalies. Specifically, the edge computing server first compares the acquired real-time power quality parameters with preset normal operating parameter ranges to identify abnormal values ​​exceeding safety thresholds. For voltage parameters, the edge computing server checks for overvoltage (e.g., above 110% of the rated value) or undervoltage (e.g., below 90% of the rated value); for current parameters, it detects for overcurrent, short circuits, or open circuits; for frequency parameters, it analyzes for frequency deviations (e.g., ±0.5Hz) or fluctuations; for power parameters, it assesses whether active and reactive power are balanced and whether the power factor is within a reasonable range. Beyond simple threshold detection, the edge computing server also applies more complex fault mode recognition algorithms, such as rule-based expert systems, statistical pattern recognition, or machine learning models, to analyze the correlation and temporal variation characteristics between parameters and identify complex fault modes. Once an anomaly is detected, the edge computing server determines the specific location of the fault using fault location algorithms, such as electrical topology analysis, fault propagation models, or triangulation based on measurement points. Finally, the edge computing server assesses the severity of the fault based on its nature, scope of impact, and potential consequences, and generates a complete fault information report containing the fault type, location, and severity.

[0038] Step S2.2: Based on real-time power quality parameters and historical power quality parameters, perform operational risk prediction and obtain risk warning information, which includes health status and fault risk level; Among them, historical power quality parameters represent power quality data recorded over a period of time (such as several hours, several days or several months), which are used to establish a baseline for normal system operation and to discover long-term trends. Operational risk forecasting refers to the process of proactively assessing the potential performance degradation or failure risks of system components in the future, based on current and historical data. Risk warning information represents the output of operational risk prediction, including an assessment of the system's health status and potential failure risks; Health status indicates the current operating condition and performance level of system components, and is usually expressed by quantitative indicators such as remaining service life, performance indicators, or health score. Failure risk level indicates the probability assessment of system components failing within a future period of time. It is usually divided into low risk, medium risk, and high risk levels and is used to guide preventive maintenance decisions.

[0039] While performing real-time fault diagnosis, edge computing servers need to conduct longer-term risk assessments to prevent potential faults. Specifically, the edge computing server first extracts historical power quality parameter data from its local database. Then, it compares and analyzes this historical data with the current real-time power quality parameters to identify trends and anomalies in these parameters over time. For energy storage units, the edge computing server assesses the state of health (SOH) and remaining lifespan of the battery by analyzing real-time voltage, current, and temperature characteristics during charge-discharge cycles. For photovoltaic power generation units, it assesses component efficiency degradation and inverter performance decline by comparing real-time output power with theoretically expected values. For wind power generation units, it assesses the health status of mechanical components and electrical systems by analyzing the relationship between real-time output power and wind speed. The edge computing server also applies predictive analytics algorithms, such as time series forecasting, regression analysis, or machine learning models, to predict future trajectories of key parameters based on historical trends and assess the potential fault risks these changes may cause. Finally, the edge computing server integrates the results of these analyses to generate risk warning information that includes health status scores and fault risk levels for each system component, providing a basis for subsequent control decisions.

[0040] Step S3: Determine control objectives based on real-time power quality parameters, fault information, and risk warning information. Control objectives include economic objectives, stability objectives, and environmental objectives. Among them, the control objective represents the expected state or performance index that the energy system of the charging station needs to achieve for optimal operation, and is the basis and foundation for the generation of collaborative control strategies; Economic objectives refer to indicators that aim to minimize costs or maximize benefits during system operation, including economic indicators such as electricity purchase costs, equipment depreciation costs, maintenance costs, and renewable energy revenue; Stability targets represent the technical indicators that a system must meet to ensure safe and stable operation, including voltage stability, frequency stability, power balance, and equipment safety operating boundaries. Environmental targets are quantitative indicators of how environmentally friendly the system is during operation, including environmental indicators such as the proportion of renewable energy consumption and carbon emissions.

[0041] After completing fault diagnosis and risk prediction, the edge computing server needs to determine the control objectives for the current system operation based on this information. Specifically, the edge computing server first analyzes the current operating status of the system, including the voltage and frequency stability of the distribution network bus, the power output of each system component, and identified fault information and risk warning information. For economic objectives, the edge computing server considers factors such as current grid electricity prices, renewable energy generation costs, energy storage charging and discharging costs, and equipment wear and tear costs to construct an economic objective function, such as minimizing the total operating cost of the charging station or maximizing economic benefits. For stability objectives, the edge computing server sets target ranges for parameters such as voltage, frequency, and power based on grid operation specifications and equipment safety operation requirements, such as maintaining the distribution network bus voltage within ±5% of the rated value and the frequency within ±0.2Hz of the rated value. For environmental objectives, the edge computing server sets renewable energy consumption ratio targets or carbon emission limits based on current renewable energy output forecasts and environmental policy requirements.

[0042] During the target determination process, the edge computing server pays particular attention to the impact of fault information and risk warning information. When a fault or high-risk state exists, the edge computing server adjusts the priority of control targets accordingly, such as increasing the weight of stability targets and decreasing the weight of economic targets to ensure the safe operation of the system. Simultaneously, the edge computing server also considers the impact of device health status on control targets; for example, for devices in poor health, their participation is appropriately reduced to avoid accelerating device degradation. Ultimately, the edge computing server forms a set of multi-dimensional control targets encompassing economic efficiency, stability, and environmental friendliness, providing a clear optimization direction for the subsequent generation of collaborative control strategies.

[0043] Step S3.1: Based on the real-time voltage and frequency values ​​in the real-time power quality parameters, calculate the voltage deviation between the real-time voltage value and the voltage reference value, and the frequency deviation between the real-time frequency value and the frequency reference value; take the minimum deviation index formed by the voltage deviation and frequency deviation as the stability target. Among them, the voltage reference value represents the expected voltage standard value under normal operating conditions of the power system, which is usually the rated voltage (such as 220V, 380V or 10kV, etc.). It is used as a reference point to measure voltage stability. The setting scheme usually refers to the State Grid or industry standards, and can be fine-tuned according to the operating environment and load characteristics of a specific charging station. The frequency reference value represents the expected standard frequency value under normal operating conditions of a power system, usually 50Hz or 60Hz. It is an important indicator for evaluating the stability of a power system, and the setting scheme usually strictly follows the power system operation procedures. The voltage deviation value represents the difference between the real-time voltage value and the voltage reference value, reflecting the degree of voltage deviation; The frequency deviation value represents the difference between the real-time frequency value and the frequency reference value, reflecting the degree of frequency deviation; Deviation index is a quantitative indicator that combines voltage deviation and frequency deviation, and is used to comprehensively evaluate the stability of the power system.

[0044] After continuously acquiring real-time power quality parameters of the distribution network bus and system components, and performing fault diagnosis and operational risk prediction, the edge computing server executes this step. Specifically, the edge computing server continuously extracts the current real-time voltage and frequency values ​​from the real-time power quality parameters. Then, the edge computing server compares these real-time values ​​with preset voltage and frequency reference values. The edge computing server calculates the difference between the real-time voltage value and the voltage reference value (voltage deviation value) and the difference between the real-time frequency value and the frequency reference value (frequency deviation value). These deviation values ​​can be directly used as absolute deviations or further converted into relative deviation percentages. The edge computing server combines these voltage and frequency deviation values ​​to form a comprehensive deviation index, which will serve as an important component of the stability objective function in the subsequent collaborative control strategy generation process.

[0045] Step S3.2: Based on the real-time electricity price information and the real-time switching power of the distribution network side in the real-time power quality parameters, calculate the total electricity cost within a unit dispatch cycle, and take the minimum total electricity cost as the economic objective; Among them, real-time electricity price information refers to the electricity price data published in real time by the electricity market or local power supply department. It usually includes the purchase price and the sales price of electricity, and can be time-of-use price, peak-valley price or real-time floating price, etc. The real-time exchange power on the distribution network side represents the power exchanged between the charging station and the external distribution network, including the power purchased from the distribution network and the power sold to the distribution network. The unit scheduling cycle represents the time interval for making a complete optimization decision and issuing control commands for the charging station energy system; Total electricity cost represents the net economic expenditure incurred between the charging station's energy system and the distribution network due to energy exchange within a unit dispatch cycle, and is used to quantify the system's economic performance. Specifically, within a unit dispatch cycle T, the total electricity cost... The calculation method is as follows: When real-time switching power When the charging station purchases electricity from the distribution network, a purchase cost is incurred; when... When the charging station sells electricity to the power grid, it receives revenue from this sale. The total electricity cost is:

[0046] in, for Real-time exchange power at any given moment (positive value for electricity purchase, negative value for electricity sale). for The real-time electricity price corresponding to the time (when) Time-based electricity purchase price ,when Electricity price for time-based electricity purchase ), Let be the unit scheduling cycle duration. In the discretized implementation, the above integral can be approximated as:

[0047] in, The sampling interval is... The sampling point number within the scheduling period. A positive value indicates net expenditure, while a negative value indicates net income.

[0048] While acquiring real-time power quality parameters, the edge computing server receives real-time electricity price information from the distribution network via an external interface and quantifies economic targets based on this information. Specifically, the edge computing server communicates with the power company's data interface or authoritative third-party data sources to obtain current and predicted distribution network electricity purchase and sales prices in real time. Simultaneously, the edge computing server extracts the real-time exchange power between the charging station and the distribution network from the real-time power quality parameters. A positive value indicates electricity purchase from the distribution network, while a negative value indicates electricity sales to the distribution network. The edge computing server multiplies the real-time exchange power by the corresponding real-time electricity price information to calculate the electricity purchase cost and electricity sales revenue within the current unit dispatch cycle (e.g., 15 minutes). For example, at a certain sampling moment, if the real-time exchange power... (A positive value indicates electricity purchased from the distribution network), with a corresponding purchase price of 0.8 yuan / kWh. Therefore, the electricity cost at that moment is 50 × 0.8 = 40 yuan / h. If the power is exchanged in real-time at another moment... (A negative value indicates electricity sold to the distribution network), with a corresponding electricity price of 0.4 yuan / kWh, then the revenue at that moment is 30 × 0.4 = 12 yuan / h. The edge computing server sums up the costs or revenues at each sampling moment within a unit scheduling cycle to obtain the total electricity cost. And use it as the economic objective function in the subsequent collaborative control strategy generation process, that is, with Minimizing this value is the direction of optimization.

[0049] In particular, this step takes minimizing the total electricity cost as the economic objective. Its technical essence is not simply financial accounting calculation, but rather using price signals as a lever to regulate the energy flow of the physical system.

[0050] In power systems, real-time electricity prices directly reflect the degree of load congestion in the distribution network (high prices typically correspond to peak load periods or times of power shortage). Therefore, edge computing servers, through optimization algorithms, minimize total electricity costs, achieving the following technical effects at the physical level: Peak shaving and valley filling: During periods of high electricity prices, reduce the amount of electricity drawn from the distribution network or use energy storage to send electricity back to the grid, thereby reducing the load rate of the distribution network transformer and alleviating the thermal effect of the lines. Energy efficiency optimization: guides the system to charge or operate at high power during periods of low electricity prices, thereby physically improving the regional power grid's capacity to absorb new energy sources.

[0051] Therefore, this economic objective is a technical constraint indicator for the energy interaction between the charging station's energy system and the external power grid, and for maintaining the balance between power grid supply and demand. The total electricity cost calculated based on this objective provides a quantitative basis for energy dispatching for the control strategy.

[0052] Step S3.3: Based on the theoretical maximum power generation, the actual power generation of the wind power generation unit, and the actual power generation of the photovoltaic power generation unit, calculate the renewable energy utilization rate, and take the maximum renewable energy utilization rate as the environmental protection target; Among them, the wind power generation unit refers to the collection of equipment in the charging station energy system that converts wind energy into electrical energy, which is usually composed of wind turbines, converters, etc. A photovoltaic power generation unit refers to a collection of devices in a charging station's energy system that converts solar energy into electrical energy; it typically consists of solar panels, inverters, and other components. The theoretical maximum power generation capacity represents the maximum electrical power that a wind power generation unit or a photovoltaic power generation unit can produce under current environmental conditions; the actual power generation capacity represents the electrical power that a wind power generation unit or a photovoltaic power generation unit actually transmits to the grid or supplies to the load at a certain moment. The renewable energy utilization rate is the ratio of the sum of the actual power generation of the wind power generation unit and the actual power generation of the photovoltaic power generation unit, divided by the sum of the theoretical maximum power generation of the wind power generation unit and the theoretical maximum power generation of the photovoltaic power generation unit. It is used to measure the system's level of clean energy absorption.

[0053] While acquiring power quality parameters in real time, the edge computing server dynamically assesses the renewable energy utilization of the charging station and uses this assessment to establish environmental protection goals. Specifically, the edge computing server uses a built-in meteorological forecasting model and mathematical models of each wind power generation unit and photovoltaic power generation unit (such as the power curve of the wind power generation unit and the IV characteristic curve of the photovoltaic module), combined with real-time meteorological data such as wind speed, light intensity, and ambient temperature, to predict the theoretical maximum power generation of each wind power generation unit and photovoltaic power generation unit. Simultaneously, the edge computing server collects the actual power generation transmitted to the distribution network bus from the inverters or converters of each wind power generation unit and photovoltaic power generation unit in real time. The edge computing server sums the actual power generation of the wind power generation unit and the actual power generation of the photovoltaic power generation unit to obtain the actual total power generation of renewable energy; it also sums the theoretical maximum power generation of the wind power generation unit and the theoretical maximum power generation of the photovoltaic power generation unit to obtain the theoretical total maximum power generation of renewable energy. The edge computing server calculates the ratio of the actual total power generation to the theoretical total maximum power generation to obtain the overall renewable energy utilization rate of the charging station, i.e.:

[0054] in, This represents the actual power generation of the wind turbine unit. This represents the actual power generation of the photovoltaic power generation unit. This represents the theoretical maximum power output of the wind turbine generator unit. This represents the theoretical maximum power output of the photovoltaic power generation unit.

[0055] The edge computing server uses the utilization rate of this renewable energy as an environmental protection target in the subsequent collaborative control strategy generation process, that is, it takes the maximization of this utilization rate as the optimization direction.

[0056] By adopting the above technical solution, the edge computing server first quantifies the stability indicators of the power grid by comparing real-time voltage and frequency data with benchmark values. Simultaneously, the edge computing server accurately calculates the total electricity cost by combining real-time electricity price information and distribution network switching power, and calculates the renewable energy utilization rate based on actual / theoretical wind and solar power. Finally, the edge computing server uses these quantified indicators as the input basis for subsequent multi-objective optimization. In summary, this embodiment transforms abstract control dimensions into calculable and comparable digital indicators, enabling the system to accurately measure the operating status under different dimensions within a unified mathematical model, providing precise data support for subsequent risk-information-based weight allocation and strategy generation.

[0057] Step S4: Generate a coordinated control strategy by weighting and integrating the economic, stability, and environmental objectives among the control targets; Among them, weighted synthesis refers to the process of mathematically combining multiple control objectives with different weights to balance the relative importance of each objective; cooperative control strategy refers to a coordinated control scheme for each system component in the charging station energy system to ensure that each component meets its own constraints while jointly achieving the optimal operation of the system as a whole.

[0058] After determining the control objectives, the edge computing server needs to transform these objectives into executable control strategies to guide the coordinated operation of various system components. Specifically, the edge computing server first determines the weight coefficients of each control objective based on the current system state, especially fault information and risk warning information. Under normal operating conditions, the edge computing server may assign higher weights to economic objectives (e.g., 0.5), and lower weights to stability and environmental objectives (e.g., 0.25 each). However, in the presence of faults or high-risk conditions, the edge computing server will increase the weight of stability objectives (e.g., 0.7) and decrease the weights of economic and environmental objectives (e.g., 0.15 each). The edge computing server uses a comprehensive optimization model, taking the weighted multi-objective function as the optimization objective, while considering the operating constraints of each system component, such as the upper and lower limits of output for wind power and photovoltaic power generation units, the state of charge (SOC) range and charging / discharging power limits for energy storage units, etc., and applies appropriate optimization algorithms, such as model predictive control (MPC) or mixed integer linear programming (MILP), to solve the optimization problem and obtain the optimal control sequence for each system component in the prediction time domain. These control sequences include the specific operating parameters of each component within each time step (e.g., 5 minutes or 15 minutes). The edge computing server also considers the synergistic effects between components, such as using energy storage units to smooth out fluctuations in photovoltaic output, or optimizing energy storage charging and discharging strategies when there are large differences in peak and valley electricity prices on the grid. Ultimately, the edge computing server generates a complete set of collaborative control strategies.

[0059] Optionally, in some embodiments, the edge computing server can also perform cross-device collaborative control based on risk propagation. This collaborative control strategy not only targets the individual device experiencing the risk but also coordinates the control of other system components.

[0060] For example, when the risk warning information detects a risk of thermal runaway caused by high-frequency harmonics in the photovoltaic inverter, the traditional protection strategy is usually to directly disconnect the photovoltaic grid. However, in the collaborative mode of this embodiment, the strategy generated by the edge computing server is: The instruction is to keep the photovoltaic power generation units connected to the grid but limit their operation to a safe power level; At the same time, the energy storage unit is instructed to immediately switch to active filtering mode or fluctuation smoothing mode, injecting reverse compensation current on the distribution network bus side to offset some of the harmonics generated by photovoltaics.

[0061] Step S4.1: Based on the fault information and risk warning information, determine the target weight coefficients for economic objectives, stability objectives, and environmental protection objectives; Among them, the target weight coefficient is the importance ratio factor assigned to the economic, stability and environmental protection targets in the comprehensive objective function. It is usually between 0 and 1, and the sum is 1. It is used to guide the optimization direction of the collaborative control strategy.

[0062] Specifically, the edge computing server receives fault information and risk warning information and assesses their impact on system operation. The weighting coefficients of the three objectives are dynamically adjusted through preset expert rules or a fuzzy logic system. For example, when a serious fault or high risk is detected, the stability weight is significantly increased (e.g., 0.7-0.9); when operating normally without faults, the weighting shifts towards economy (e.g., 0.4-0.6) and environmental friendliness (e.g., 0.2-0.3), while maintaining the baseline stability weight.

[0063] Step S4.2: Calculate the comprehensive objective function value of the control target based on the target weight coefficient, and identify the target operation mode of the charging station energy system based on the comprehensive objective function value. The target operation modes include environmental protection priority mode, economic priority mode and stability priority mode. Among them, the comprehensive objective function value is a single value obtained by weighting the economic, stability, and environmental protection objectives according to their weights, and is used to quantify the comprehensive optimization effect; the target operation mode of the charging station energy system refers to the current optimization direction of the charging station (environmental protection priority mode, economic priority mode, or stability priority mode), which is a major guideline for the generation of subsequent specific control strategies.

[0064] Specifically, the edge computing server, based on the weighting coefficients determined in step S4.1, performs a weighted summation of the three objective functions constructed in step S3 to calculate the comprehensive objective function value. The calculation formula is as follows:

[0065] in, , This represents the economic objective function value (after normalization). The value of the stability objective function. The objective function value is the environmental protection objective.

[0066] The edge computing server then uses this comprehensive value and its weighted allocation to identify the target operating mode of the current charging station's energy system. The discrimination mechanism is typically based on the principle of maximum weight: if the stability objective has the highest weight coefficient, it is identified as the stability-first mode; if the economic objective has the highest weight coefficient, it is identified as the economic-first mode; and if the environmental objective has the highest weight coefficient, it is identified as the environmental-first mode. This mechanism provides a high-level decision-making framework to ensure that the mode selection is consistent with real-time conditions.

[0067] Optionally, in some embodiments, the edge computing server may employ multi-objective optimization (MOO) technology to calculate the comprehensive objective function value and identify patterns through Pareto front analysis or analytic hierarchy process (AHP). The edge computing server treats the three objectives as independent optimization dimensions and runs the MOO algorithm to find the Pareto optimal solution set. Based on the weight coefficients determined in S4.1, the edge computing server selects the solution that best meets the current weight requirements from the Pareto optimal solution set; this solution contains the current objective operation pattern.

[0068] Step S4.3: Based on the relationship between the target operating mode and the current operating mode of the charging station's energy system, determine the final target operating mode of the charging station's energy system, including: Step S4.3.1: When the target operating mode is different from the current operating mode of the charging station's energy system, start the mode switching timer and record the target operating mode as the operating mode to be switched; Among them, the current operating mode of the charging station energy system refers to the type of optimization strategy that the system is executing; the mode switching timer is used to set the waiting time before switching from one mode to another to prevent frequent switching; the preset switching duration is the time threshold before the timer triggers the mode switching, and its setting scheme needs to take into account system inertia, equipment response time and avoid repeated switching losses (usually 5-15 minutes); the operating mode to be switched is the new target mode, but it has not been officially switched yet due to the function of the timer.

[0069] Specifically, after identifying the target operating mode, if it differs from the current mode, the edge computing server will not switch immediately to avoid frequent switching due to momentary fluctuations. Instead, it will start a mode switching timer and record this target mode as the operating mode to be switched to. Only after the timer reaches a preset switching duration will the edge computing server further evaluate whether to perform the mode switch. For example, if the current priority is economy and the target is stability priority, the edge computing server will start a timer and record stability priority as the mode to be switched to.

[0070] Step S4.3.2: When the timing duration of the mode switching timer reaches the preset switching duration, real-time fault information and risk warning information are reacquired, the updated comprehensive objective function value is calculated based on the reacquired fault information and risk warning information, and the updated target operating mode is determined according to the updated comprehensive objective function value. Among them, updating the comprehensive objective function value refers to the comprehensive objective function value recalculated by the edge computing server based on the latest fault and risk warning information after the mode switching time reaches the preset value; Updating the target operating mode refers to the target operating mode that the edge computing server re-evaluates and determines based on this updated value, which is used to confirm the necessity and direction of mode switching.

[0071] Specifically, when the mode switching timer reaches the preset switching duration, the edge computing server triggers a mode reassessment. The edge computing server re-executes step S2 to obtain the latest power quality parameters and performs fault diagnosis and risk prediction to ensure the accuracy of the information. Then, based on this updated information, the edge computing server re-executes steps S4.1 and S4.2, namely, redetermining the target weight coefficients, calculating the "updated comprehensive objective function value", and identifying the "updated target operating mode".

[0072] Step S4.3.3: If the target running mode to be updated is the same as the running mode to be switched, then switch the current running mode to the running mode to be switched. Specifically, after re-evaluating in step S4.3.2, the edge computing server compares the target operating mode with the operating mode to be switched. If both are the same (e.g., both are "stability priority mode"), it indicates that the initial mode switching requirement is stable and not a transient disturbance. The edge computing server will then formally execute the mode switch: updating the current operating mode of the charging station's energy system to the operating mode to be switched. For example, if the current mode is "economy priority mode," and both the mode to be switched and the target mode are "stability priority mode," then the edge computing server will formally switch the current operating mode to "stability priority mode" and subsequently enable the corresponding stability priority control strategy.

[0073] Step S4.3.4: If the target operating mode to be updated is different from the operating mode to be switched, keep the current operating mode unchanged and reset the duration of the mode switching timer to zero; Specifically, after the edge computing server re-evaluates in step S4.3.2, if the updated target operating mode is inconsistent with the initially recorded operating mode to be switched to—for example, the initial goal was to switch from "economic priority mode" to "stability priority mode" (the operating mode to be switched to), but after waiting for the preset switching time, the re-evaluation finds that the most reasonable target operating mode has returned to "economic priority mode" or "environmental priority mode" (the updated target operating mode)—executing the initial mode switch would be unreasonable and might even lead to new instability. In this case, the edge computing server adopts a conservative strategy: maintaining the current operating mode and continuing to execute the original control strategy. Simultaneously, the edge computing server resets the mode switching timer to zero, clears the current switching attempt, and ensures that future switching decisions are based on the necessity of continuous verification.

[0074] When the edge computing server detects a change in the target operating mode, it does not act immediately. Instead, it starts a mode switching timer and marks the new mode as a pending switch, avoiding frequent and unnecessary mode switching caused by momentary system disturbances or brief data fluctuations. After the timer reaches a preset duration, the edge computing server forcibly re-acquires the latest fault and risk information for a secondary assessment. Then, the edge computing server recalculates the target mode and compares it with the previously pending switch mode. The switch is only executed if both match; otherwise, the timer is reset. This ensures that each mode switch is based on a stable and verified system condition, improving the stability, reliability, and security of the control strategy, guaranteeing a smooth transition of the control system's operating mode, and avoiding additional wear and tear on the equipment caused by frequent switching of operating states.

[0075] Step S4.4: When the final target operating mode is the environmental protection priority mode, generate a wind and solar power priority control strategy, including: Based on the rated transmission power of the distribution bus and the real-time load power of the distribution bus in the real-time power quality parameters, calculate the remaining connectable power of the distribution bus. When the sum of the actual power generation of the wind power generation unit and the photovoltaic power generation unit does not exceed the remaining available power, the wind power generation unit and the photovoltaic power generation unit are controlled to output in maximum power point tracking mode. When the sum of the actual power generation of the wind power generation unit and the photovoltaic power generation unit exceeds the remaining available power, and the state of charge of the energy storage unit is lower than the upper limit threshold for charging, the electrical energy exceeding the remaining available power will be transferred to the energy storage unit for storage. When the sum of the actual power generation of the wind power generation unit and the photovoltaic power generation unit exceeds the remaining available power, and the state of charge of the energy storage unit is greater than or equal to the charging upper limit threshold, the output power of the wind power generation unit and the photovoltaic power generation unit is limited to the remaining available power.

[0076] Among them, the wind and solar power priority control strategy refers to the strategy of prioritizing the maximization of output of wind power generation units and photovoltaic power generation units in this mode, and reasonably coordinating the energy storage units for consumption; the rated transmission power of the distribution network bus indicates the maximum electrical power that the distribution network bus can safely and stably transmit; the remaining accessible power refers to the additional power that the distribution network bus can still safely accept under the current load; the maximum power point tracking mode output (MPPT) refers to the photovoltaic or wind power generation unit adjusting its operating point to achieve its theoretical maximum output power; the charging upper limit threshold indicates the maximum state of charge (SOC) that the energy storage unit is allowed to reach, which is a key parameter to avoid battery overcharging. Its setting scheme is usually set by the battery management system (BMS) according to the battery type and life requirements, such as 90%-95% SOC.

[0077] Specifically, in the environmental priority mode, the core task of the edge computing server is to maximize the utilization of wind and solar resources. The edge computing server first obtains the current load power and rated transmission power of the distribution network bus, and calculates the remaining connectable power (rated transmission power minus current load power). Then, the edge computing server obtains the sum of the actual generated power of the wind power generation units and photovoltaic power generation units, and handles the situation according to different scenarios: If the sum of wind and solar power does not exceed the remaining connectable power, the edge computing server instructs the wind and solar units to perform MPPT output. If the sum exceeds the limit but the SOC of the energy storage unit is lower than the charging upper limit threshold, the edge computing server prioritizes transferring the excess to the energy storage unit. If the sum exceeds the limit and the SOC of the energy storage unit has reached the charging upper limit threshold, the edge computing server limits the output power of the wind and solar units to the remaining connectable power to ensure grid security.

[0078] Optionally, in some embodiments, when the distribution bus voltage in the power quality parameters deviates from the rated voltage by more than a voltage deviation threshold, the edge computing server adjusts the reactive power output of the wind power generation unit and the photovoltaic power generation unit to compensate for the voltage deviation.

[0079] The edge computing server first determines the grid's absorption boundary by combining the real-time carrying capacity of the distribution network bus, i.e., the remaining available power, to intelligently schedule wind and solar power generation units. Then, while ensuring grid safety and preventing overload, the edge computing server prioritizes maximizing the use of renewable energy through maximum power point tracking (MPPT). When power generation exceeds the grid's capacity, the edge computing server prioritizes storing excess energy in energy storage units, rather than directly curtailing wind and solar power. Instead, it assesses the state of charge (SOC) of the energy storage units; if not full, it transfers the excess energy for storage. Finally, the edge computing server only restricts wind and solar output when the energy storage is full and power output is still exceeding limits. In summary, this embodiment, through this tiered absorption mechanism, minimizes renewable energy waste by utilizing energy storage as a buffer, while strictly adhering to grid safety boundaries, thereby improving the overall energy efficiency and environmental benefits of the charging station's energy system.

[0080] Step S4.5: When the final target operating mode is the economy priority mode, generate an energy storage economic dispatch control strategy, including: Based on real-time electricity price information from the distribution network side and preset electricity price thresholds, high-price and low-price periods are identified; During periods of low electricity prices and when the state of charge of the energy storage unit is below the charging target threshold, the energy storage unit is controlled to charge at the rated charging power. During periods of high electricity prices and when the state of charge of the energy storage unit is higher than the lower discharge threshold, the energy storage unit is controlled to discharge at the rated discharge power.

[0081] Among them, the energy storage economic dispatch control strategy refers to the strategy of optimizing the economic benefits of the system by utilizing the charging and discharging operations of energy storage units, through arbitrage based on electricity price differences or peak shaving and valley filling; the preset electricity price threshold is used to distinguish between high / low electricity price periods, and its setting scheme is usually determined based on historical electricity prices and market rules (it can be a fixed value or a dynamic threshold); the charging target threshold represents the target SOC that the energy storage unit should reach when charging at low electricity prices, and is usually determined based on battery life and system requirements (such as within the range of 20%-80% SOC); the discharge lower limit threshold represents the lowest SOC that the energy storage unit is allowed to reach during the discharge process, which is used to protect the battery, and its setting scheme is set by the BMS according to the battery type and safety standards (such as 10%-15% SOC).

[0082] Specifically, in the economy-first mode, the core function of the edge computing server is to optimize energy storage charging and discharging by utilizing electricity price differences. The edge computing server continuously acquires real-time electricity price information from the distribution network and compares it with preset electricity price thresholds to identify high / low electricity price periods. Simultaneously, the edge computing server monitors the state of charge (SOC) of the energy storage unit in real time. If it is a low electricity price period and the energy storage unit's SOC is below the charging target threshold (e.g., 80%), the edge computing server instructs the energy storage unit to purchase electricity from the grid at its rated charging power for fast charging, storing low-priced energy. If it is a high electricity price period and the energy storage unit's SOC is above the discharge lower limit threshold (e.g., 20%), the edge computing server instructs the energy storage unit to supply power to the load or sell electricity to the grid at its rated discharge power to avoid high prices or generate revenue.

[0083] Optionally, in some embodiments, when the rate of change of the distribution bus voltage in the power quality parameters exceeds the voltage change rate threshold, the edge computing server adjusts the charging or discharging power of the energy storage unit to suppress voltage fluctuations.

[0084] Edge computing servers acquire real-time electricity price information from the distribution network and accurately identify high-price and low-price periods based on preset price thresholds, thereby intelligently controlling the charging and discharging behavior of energy storage units. During low-price periods and when the energy storage is not fully charged, the edge computing server controls the energy storage units to charge at rated power, storing low-cost energy. During high-price periods and when the energy storage has sufficient power, the edge computing server controls its discharging, replacing expensive grid-purchased electricity. Therefore, this embodiment utilizes peak-valley price differences to guide energy transfer at the physical level, not only reducing system operating costs but also achieving peak shaving and valley filling at the microscopic level, alleviating the power supply pressure on the grid during peak hours.

[0085] Step S4.6: When the final target operating mode is stability-first mode, generate a voltage and frequency stability control strategy, including: Obtain the charging demand information of each charging terminal currently connected to the charging unit. The charging demand information includes the charging terminal identifier, current state of charge, and planned charging end time. Based on charging demand information, the charging priority is calculated for each charging terminal. The charging priority is negatively correlated with the current state of charge and negatively correlated with the time difference between the planned end of charging and the current time. Calculate the currently available charging power based on the remaining available power of the wind power generation unit and the photovoltaic power generation unit and the dischargeable power of the energy storage unit; Based on the charging priority from high to low, charging power is allocated to each charging terminal in sequence until the currently available charging power is allocated or the charging needs of all charging terminals are met.

[0086] Among them, the voltage and frequency stability control strategy refers to the strategy of maintaining voltage and frequency stability by flexibly scheduling charging load and coordinating distributed power sources and energy storage in this mode to quickly respond to grid fluctuations; charging demand information includes charging terminal identification, electric vehicle current state of charge and planned charging end time; charging priority is an indicator that measures the order in which each terminal is preferred during power adjustment, and is negatively correlated with the time difference between the state of charge and the planned end time and the current time (the lower the time difference and the closer the time difference, the higher the priority); remaining available power is the additional power that wind and solar units can provide in addition to meeting their own operation; the discharge power of energy storage units refers to the maximum power that energy storage can currently release; the currently allocable charging power is the total power that the charging station can safely allocate to charging terminals under the premise of ensuring grid stability, and its setting scheme is usually dynamically calculated based on real-time grid status, total charging station capacity and dispatchable power.

[0087] Specifically, in stability-first mode, the core function of the edge computing server is to flexibly schedule charging loads and coordinate power supply. The edge computing server first obtains the charging demand information (identifier, SOC, planned end time) of each charging terminal. Based on this information, the edge computing server calculates the charging priority for each terminal according to the principle that lower SOC and closer planned end time mean higher priority. Simultaneously, the edge computing server calculates the remaining available power of wind power generation units and photovoltaic power generation units, as well as the discharge power of energy storage units, and comprehensively evaluates and determines the currently allocable charging power. The calculation formula is as follows:

[0088] in, For the first The current state of charge of each charging terminal. The time difference between the planned end of charging and the current time. and These are preset weighting coefficients. Finally, the edge computing server allocates charging power to each charging terminal in descending order of priority until all available power is exhausted or the needs of all terminals are met. This ensures load reduction when the power grid needs it, while also taking into account user experience.

[0089] Optionally, in some embodiments, when the load power of the distribution bus exceeds the upper limit of the load power and there is a charging terminal with bidirectional charging and discharging function, the edge computing server sends a reverse discharge request to the charging terminal with bidirectional charging and discharging function, and controls the charging terminal to discharge to the distribution bus after receiving the confirmation response.

[0090] The edge computing server first obtains the state of charge and planned end time of each charging terminal, and calculates the charging priority accordingly, ensuring that vehicles in urgent need are charged first. Then, the edge computing server calculates the remaining power of wind and solar power and the dischargeable power of energy storage, determining the total power quota that the system can safely allocate. Next, the edge computing server strictly allocates quotas according to priority, from highest to lowest, until the quota is exhausted. Finally, low-priority demands are temporarily suppressed or reduced. In summary, when resources are limited or the system is unstable, through refined load-side orderly management, the total load is constrained while ensuring the needs of critical users, preventing disorderly accumulation of charging loads from impacting the distribution network bus and effectively maintaining the stability of grid voltage and frequency.

[0091] By adopting the technical solution in step S4 above, the edge computing server dynamically adjusts the weight coefficients of each control objective based on real-time fault information and risk warning information. This enables the charging station energy system to adjust its optimization focus based on its actual operational health status and risk level. Through this adaptive weighting mechanism, combined with the evaluation of the comprehensive objective function value, the edge computing server can identify the optimal operating mode of the charging station energy system and trigger corresponding customized control strategies (such as wind and solar priority control strategy, energy storage economic dispatch control strategy, and voltage and frequency stability control strategy). In summary, this embodiment establishes an adaptive switching mechanism for operating modes driven by equipment health status, avoiding the limitations of a single optimization objective, realizing intelligent switching and high adaptability of control strategies in different scenarios, and improving the robustness and environmental adaptability of the charging station energy system.

[0092] Step S5: After generating control commands using the collaborative control strategy to control the collaborative operation of system components, collect the real-time power quality parameters of the distribution network bus and system components again as the power quality parameters after execution. Among them, the power quality parameters after execution refer to the power quality parameters of the distribution bus and system components that are re-collected after the control command is executed by the system components. They are used to evaluate the execution effect of the control command and the system response.

[0093] After generating the collaborative control strategy, the edge computing server needs to translate the strategy into specific control commands and distribute them to each system component, while simultaneously monitoring the execution effect of the commands. Specifically, the edge computing server first converts the optimized collaborative control strategy into a command format recognizable by the controllers of each system component. For wind power generation units, the edge computing server generates power limiting commands or switch-in / switch-out commands; for photovoltaic power generation units, the edge computing server generates power limiting commands, maximum power point tracking (MPPT) enable / disable commands, or reactive power regulation commands; for energy storage units, the edge computing server generates charging / discharging power commands, state of charge (SOC) target value commands, or operating mode switching commands; for charging units, the edge computing server generates charging power allocation commands, charging priority commands, or charging time scheduling commands. The edge computing server distributes these control commands to the local controllers of each system component via industrial communication networks (such as industrial Ethernet, Modbus TCP / IP, CAN bus, etc.). To ensure reliable instruction transmission, the edge computing server implements a communication confirmation mechanism, requiring each component controller to return confirmation information after receiving an instruction. If no confirmation is received within a preset timeout period, the edge computing server will resend the instruction or trigger a communication exception handling process.

[0094] After the control command is issued and execution begins, the edge computing server waits for a preset stabilization time (e.g., a few seconds to tens of seconds, depending on the system response characteristics) and then reactivates the data acquisition process to obtain the latest power quality parameters of the distribution network bus and various system components, including voltage, current, frequency, and power. These newly acquired parameters are labeled as "post-execution power quality parameters" and compared with the parameters before control to evaluate the execution effect of the control command and the system's response. The edge computing server stores these post-execution parameters in a database as the initial state for the next control cycle and as the basis for evaluating the control effect.

[0095] Step S6: Calculate the parameter deviation between the power quality parameters and the control target after execution. When the parameter deviation exceeds the preset deviation threshold, return to step S4, adjust the collaborative control strategy, and reissue the control command to control the collaborative operation of system components.

[0096] Among them, parameter deviation represents the difference between the power quality parameters after execution and the control target, and is used to evaluate the execution effect of control commands and the accuracy of system response; parameter deviation includes voltage deviation, frequency deviation and power deviation. After acquiring the power quality parameters after execution, the edge computing server needs to evaluate the execution effect of the control commands and determine whether the system has achieved the expected control objectives. Specifically, the edge computing server extracts the control target value determined in step S3 and calculates the deviations of various parameters. For voltage deviation, the edge computing server calculates the difference between the executed voltage value of the distribution network bus and each key node and the target voltage value, which can be calculated using the absolute difference method: ΔV = |V_executed - V_target|, or the relative deviation method: δV = |V_executed - V_target| / V_target × 100%. For frequency deviation, the edge computing server calculates the difference between the executed value of the system frequency and the target value, similarly using the absolute difference method or the relative deviation method. For power deviation, the edge computing server calculates the difference between the executed power output of each system component and the target power value, including active power deviation and reactive power deviation. The edge computing server may also calculate deviations of other key parameters, such as the state of charge (SOC) deviation of energy storage units and the charging power distribution deviation of charging units.

[0097] Among them, the preset deviation threshold represents the upper limit of the parameter deviation allowed by the system and is the standard for judging whether the control effect meets the requirements. The setting of the preset deviation threshold should take into account the system stability requirements, equipment response characteristics and control accuracy requirements. Typically, the voltage deviation threshold can be set to ±2% to ±5% of the rated value, the frequency deviation threshold can be set to ±0.1Hz to ±0.2Hz of the rated value, and the power deviation threshold can be set to ±5% to ±10% of the target value. The specific values ​​can be adjusted according to the power grid operation specifications and the actual situation of the charging station. Adjusting the coordinated control strategy means modifying or optimizing the original control strategy according to the parameter deviation to reduce the parameter deviation and improve the control accuracy.

[0098] After calculating parameter deviations, the edge computing server needs to determine whether the control effect meets the requirements. Specifically, the edge computing server compares the deviations of various parameters with preset thresholds, such as voltage deviation threshold (±3%), frequency deviation threshold (±0.15Hz), and power deviation threshold (±7%). If any deviation exceeds the threshold, the edge computing server analyzes the cause, which may include model errors, external disturbances, or device response delays. The edge computing server takes corresponding adjustments based on the deviation type: updating model parameters, enhancing control robustness, or adjusting control gain. The edge computing server reruns the optimization algorithm, generates a new strategy, and issues instructions to form closed-loop control until the deviation falls within the threshold or the maximum number of iterations is reached.

[0099] Optionally, in some embodiments, the edge computing server can also use the parameter deviation after the control command is executed to reverse correct the risk warning information, forming a control-diagnosis dual closed loop.

[0100] Specifically, when the edge computing server issues a power adjustment command to a system component (such as a charging unit), if it detects that the deviation between the power quality parameters (such as voltage response value) after execution and the expected control target continues to exceed a preset threshold, the edge computing server will not only adjust the current control strategy, but also determine that the "response model" of the system component has drifted.

[0101] The server uses this deviation characteristic as new input data and feeds it back to the risk prediction module, which then infers that the component may have a hidden "control failure risk" or "communication delay fault." Based on this inference, the system automatically increases the failure risk level of the component and marks it as an untrusted source in the next control cycle, thereby eliminating or significantly reducing the component's participation when calculating the weighted comprehensive objective. This mechanism ensures that the control system can dynamically identify those nominally healthy but actually uncontrollable potential devices, further enhancing the system's robustness.

[0102] In this embodiment of the invention, by acquiring power quality parameters of the distribution network bus and system components in real time, performing fault diagnosis and operational risk prediction based on power quality parameters, determining control objectives based on power quality parameters, fault information, and risk warning information, generating collaborative control strategies for system components based on control objectives, and calculating parameter deviations based on re-acquired post-execution power quality parameters after the control command is executed, adjusting the collaborative control strategy and re-executing the control command issuance step when the parameter deviation exceeds a preset deviation threshold, the edge computing server can not only solve the communication latency problem, but also directly incorporate the health status and potential risks of equipment into the real-time control closed loop, thereby dynamically adjusting and optimizing the objectives. This solves the problem in related technologies that only focus on grid parameters while ignoring equipment health risks, and causing secondary damage due to forced scheduling of sub-healthy equipment, thereby improving the operational safety and reliability of the charging station energy system and extending the service life of key equipment.

[0103] In this embodiment of the invention, the target weight coefficients for economic, stability, and environmental objectives are determined based on fault information and risk warning information. The target operating mode of the charging station energy system is identified based on the comprehensive objective function value, generating corresponding wind and solar power priority control strategies, energy storage economic dispatch control strategies, and voltage and frequency stability control strategies. Simultaneously, after identifying the target operating mode, a mode switching timer is introduced. The mode switching is then reconfirmed through information reacquisition and recalculation mechanisms. Therefore, the system can adaptively adjust the optimization focus based on the actual equipment health status (e.g., automatically sacrificing economic efficiency to maintain stability during high-risk situations) and filter out erroneous switching caused by instantaneous disturbances. This solves the problems of traditional solutions using fixed weights that cannot balance equipment protection and operational efficiency, as well as system oscillations caused by frequent control strategy switching. Consequently, the robustness, stability, and intelligence of the charging station energy system under complex operating conditions are improved.

[0104] Example 2 This embodiment provides a charging station energy system, including an edge computing server, a distribution network bus, and system components. The system components include at least a wind power generation unit, a photovoltaic power generation unit, an energy storage unit, and a charging unit. The edge computing server specifically includes: one or more processors and a memory; the memory is used to store computer programs, and when the computer programs are executed by the processors, they implement the multi-source collaborative control method for charging stations based on edge computing described in Embodiment 1 above.

[0105] It should be noted that, Figure 2 The structure of the edge computing server shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0106] like Figure 2 As shown, the edge computing server includes a CPU 201, which can perform various appropriate actions and processes based on a program stored in the read-only memory ROM 202 or a program loaded from the storage section 208 into the random access memory RAM 203, such as performing the methods described in the above embodiments. The RAM 203 also stores various programs and data required for system operation. The CPU 201, ROM 202, and RAM 203 are interconnected via a bus 204. An I / O interface 205 is also connected to the bus 204.

[0107] The following components are connected to I / O interface 205: input section 206 including audio input devices, push-button switches, etc.; output section 207 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 208 including a hard disk, etc.; and communication section 209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 209 performs communication processing via a network such as the Internet. Drive 210 is also connected to I / O interface 205 as needed. Removable media 211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 210 as needed so that computer programs read from them can be installed into storage section 208 as needed.

[0108] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 209, and / or installed from removable medium 211. When the computer program is executed by CPU 201, it performs the various functions defined in the present invention.

[0109] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the edge computing-based multi-source collaborative control method for charging stations as described in Embodiment 1 above.

[0110] Example 4 This embodiment provides a computer program product or computer program, including computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the edge computing-based multi-source collaborative control method for charging stations described in Embodiment 1 above.

[0111] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0112] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0116] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-source collaborative control method for charging stations based on edge computing, characterized in that, include: The raw operating data of the distribution network bus and system components are acquired in real time and preprocessed to obtain real-time power quality parameters; Fault diagnosis and operational risk prediction are performed based on real-time power quality parameters to obtain fault information and risk warning information. Control objectives are determined based on real-time power quality parameters, fault information, and risk warning information. These objectives include economic objectives, stability objectives, and environmental objectives. A coordinated control strategy is generated by weighting and integrating the economic, stability, and environmental objectives within the control targets. After the coordinated control strategy generates control commands to control the coordinated operation of system components, the real-time power quality parameters of the distribution network bus and system components are collected again as the power quality parameters after execution. The system calculates the parameter deviation between the power quality parameters and the control target after execution. When the parameter deviation exceeds the preset deviation threshold, the system adjusts the collaborative control strategy and reissues the control command to control the collaborative operation of system components.

2. The multi-source collaborative control method for charging stations based on edge computing as described in claim 1, characterized in that, Control objectives are determined based on real-time power quality parameters, fault information, and risk warning information. These objectives include economic, stability, and environmental goals, among others. Based on the real-time voltage and frequency values ​​in the real-time power quality parameters, the voltage deviation between the real-time voltage value and the voltage reference value, and the frequency deviation between the real-time frequency value and the frequency reference value are calculated respectively; the minimum deviation index formed by the voltage deviation and frequency deviation is taken as the stability target. Based on the real-time electricity price information and real-time power quality parameters of the distribution network, the total electricity cost within a unit dispatch cycle is calculated, with the minimum total electricity cost as the economic objective. Based on the theoretical maximum power generation, the actual power generation of the wind power generation unit, and the actual power generation of the photovoltaic power generation unit, the renewable energy utilization rate is calculated, with the maximum renewable energy utilization rate as the environmental protection target.

3. The multi-source collaborative control method for charging stations based on edge computing as described in claim 1, characterized in that, The process involves weighting and synthesizing the economic, stability, and environmental objectives within the control targets to generate a coordinated control strategy, including: Based on fault information and risk warning information, determine the target weight coefficients for economic objectives, stability objectives, and environmental protection objectives; Based on the target weight coefficient, the comprehensive objective function value of the control target is calculated, and the target operation mode of the charging station energy system is identified based on the comprehensive objective function value. The target operation modes include environmental protection priority mode, economic priority mode and stability priority mode. Based on the relationship between the target operating mode and the current operating mode of the charging station's energy system, the final target operating mode of the charging station's energy system is determined. When the final target operating mode is the environmental protection priority mode, a wind and solar power generation priority control strategy is generated. When the final target operating mode is the economy priority mode, an energy storage economic dispatch control strategy is generated. When the final target operating mode is stability-first mode, a voltage and frequency stability control strategy is generated.

4. The multi-source collaborative control method for charging stations based on edge computing as described in claim 3, characterized in that, Based on the relationship between the target operating mode and the current operating mode of the charging station's energy system, the final target operating mode of the charging station's energy system is determined, including: When the target operating mode is different from the current operating mode of the charging station's energy system, the mode switching timer is started, and the target operating mode is recorded as the operating mode to be switched to. When the mode switching timer reaches the preset switching duration, real-time fault information and risk warning information are reacquired. Based on the reacquired fault information and risk warning information, the updated comprehensive objective function value is calculated and the updated target operating mode is determined according to the updated comprehensive objective function value. If the target operating mode for updating is the same as the operating mode to be switched, then the current operating mode will be switched to the operating mode to be switched. If the target operating mode to be updated is different from the operating mode to be switched, the current operating mode will remain unchanged, and the duration of the mode switching timer will be reset to zero.

5. The multi-source collaborative control method for charging stations based on edge computing as described in claim 3, characterized in that, When the final target operating mode is environmental protection priority mode, a wind and solar power priority control strategy is generated, including: Based on the rated transmission power of the distribution bus and the real-time load power of the distribution bus in the real-time power quality parameters, calculate the remaining connectable power of the distribution bus. When the sum of the actual power generation of the wind power generation unit and the photovoltaic power generation unit does not exceed the remaining available power, the wind power generation unit and the photovoltaic power generation unit are controlled to output in maximum power point tracking mode. When the sum of the actual power generation of the wind power generation unit and the photovoltaic power generation unit exceeds the remaining available power, and the state of charge of the energy storage unit is lower than the upper limit threshold for charging, the electrical energy exceeding the remaining available power will be transferred to the energy storage unit for storage. When the sum of the actual power generation of the wind power generation unit and the photovoltaic power generation unit exceeds the remaining available power, and the state of charge of the energy storage unit is greater than or equal to the charging upper limit threshold, the output power of the wind power generation unit and the photovoltaic power generation unit is limited to the remaining available power.

6. The multi-source collaborative control method for charging stations based on edge computing as described in claim 3, characterized in that, When the final target operating mode is the economy-first mode, an energy storage economic dispatch control strategy is generated, including: Based on real-time electricity price information from the distribution network side and preset electricity price thresholds, high-price and low-price periods are identified; During periods of low electricity prices and when the state of charge of the energy storage unit is below the charging target threshold, the energy storage unit is controlled to charge at the rated charging power. During periods of high electricity prices and when the state of charge of the energy storage unit is higher than the lower discharge threshold, the energy storage unit is controlled to discharge at the rated discharge power.

7. The multi-source collaborative control method for charging stations based on edge computing as described in claim 3, characterized in that, When the final target operating mode is stability-first mode, a voltage and frequency stability control strategy is generated, including: Obtain the charging demand information of each charging terminal currently connected to the charging unit. The charging demand information includes the charging terminal identifier, current state of charge, and planned charging end time. Based on charging demand information, the charging priority is calculated for each charging terminal. The charging priority is negatively correlated with the current state of charge and negatively correlated with the time difference between the planned end of charging and the current time. Calculate the currently available charging power based on the remaining available power of the wind power generation unit and the photovoltaic power generation unit and the dischargeable power of the energy storage unit; Based on the charging priority from high to low, charging power is allocated to each charging terminal in sequence until the currently available charging power is allocated or the charging needs of all charging terminals are met.

8. A charging station energy system, characterized in that, It includes edge computing servers, distribution network busbars, and system components, with system components including at least wind power generation units, photovoltaic power generation units, energy storage units, and charging units; The edge computing server includes a processor and a memory; the memory is used to store computer programs, which, when executed by the processor, implement a multi-source collaborative control method for charging stations based on edge computing as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the multi-source collaborative control method for charging stations based on edge computing as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps in the edge computing-based multi-source collaborative control method for charging stations as described in any one of claims 1-7.

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