Micro-grid regulation capability evaluation system and method based on light, storage and charging collaborative interaction

Abstract

The invention belongs to the technical field of optical storage and charging collaborative interaction, and particularly relates to a microgrid regulation capability evaluation system and method based on optical storage and charging collaborative interaction, and the system comprises a multi-source data fusion module, a dynamic evaluation module, a collaborative optimization module, a digital twin verification module and an output regulation and control module. The signal output end of the multi-source data fusion module is connected with the dynamic evaluation module, the signal output end of the dynamic evaluation module is connected with the collaborative optimization module, and the signal output end of the collaborative optimization module is connected with the digital twin verification module. And the signal output end of the digital twin verification module is connected with the output regulation and control module. And the mixed spatio-temporal data fusion technology reduces the synchronization error of the multi-source data. And a dynamic evaluation-optimization combined solving method is adopted, so that the calculation complexity is reduced. According to the event-driven rolling optimization mechanism, the photovoltaic output volatility and the load abrupt change rate are defined as trigger threshold values, only the affected subsystem is re-evaluated, and the calculation amount is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic-storage-charging collaborative interaction, and specifically to a microgrid regulation capability evaluation system and method based on photovoltaic-storage-charging collaborative interaction. Background Art

[0002] A photovoltaic (PV)-storage-charging microgrid is a microgrid system that combines photovoltaic power generation, energy storage systems, and charging facilities. When sunlight is sufficient, the PV system generates electricity, some of which is directly supplied to the charging facilities to charge electric vehicles, while the excess is stored in the energy storage system. When sunlight is insufficient or charging demand is high, and the PV system cannot meet all electricity needs, the energy storage system releases stored energy and works with the PV system to power the charging facilities, ensuring continuous and stable charging service. Furthermore, the PV-storage-charging microgrid can optimize power distribution and achieve efficient energy utilization through an intelligent control system based on real-time information such as photovoltaic power generation, energy storage capacity, and charging demand.

[0003] The photovoltaic storage and charging microgrid regulation system achieves energy balance and grid stability through the coordinated interaction of photovoltaics (PV), energy storage (ESS), and charging piles (EV).

[0004] The current PV-storage-charging microgrid regulation system has the following defects, which lead to its insufficient capacity:

[0005] Data heterogeneity: photovoltaic, energy storage, and charging pile equipment have diverse data formats (such as Modbus, CAN, and IEC61850), and communication protocols are incompatible, resulting in low data collection efficiency and poor real-time performance.

[0006] Lack of dynamic evaluation model

[0007] Traditional static models cannot accurately quantify dynamic coupling effects such as light fluctuations, nonlinear attenuation of energy storage SOC, and sudden changes in charging load, and the error in regulating capability evaluation is large. Summary of the Invention

[0008] The purpose of the present invention is to provide a microgrid regulation capability evaluation system and method based on the synergistic interaction of photovoltaic storage and charging, so as to solve the problems raised in the above background technology.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging, comprising:

[0010] Multi-source data fusion module, dynamic evaluation module, collaborative optimization module, digital twin verification module and output control module;

[0011] Among them, the signal output end of the multi-source data fusion module is connected to the dynamic evaluation module, the signal output end of the dynamic evaluation module is connected to the collaborative optimization module, the signal output end of the collaborative optimization module is connected to the digital twin verification module, and the signal output end of the digital twin verification module is connected to the output control module.

[0012] Preferably, the multi-source data fusion module solves data heterogeneity and realizes unified access and preprocessing of real-time data. The multi-source data fusion module includes a protocol adaptation layer, a data cleaning unit, and a spatiotemporal alignment engine.

[0013] Preferably, the protocol adaptation layer: supports Modbus / TCP, MQTT, and OPCUA conversion, and unifies the original data into JSON format;

[0014] The data cleaning unit removes abnormal data based on sliding window mean filtering and isolation forest algorithm, and the window size of the sliding window mean is 10s;

[0015] The spatiotemporal alignment engine uses Newton interpolation to align data with different sampling frequencies and compensates for communication delays through Kalman filtering.

[0016] The multi-source data fusion module inputs: photovoltaic output, energy storage SOC, and charging pile power raw data, and outputs: standardized data stream with time synchronization and unified format.

[0017] Preferably, the dynamic evaluation module quantifies the real-time regulation capability of the microgrid to solve the static model error problem. The dynamic evaluation module includes a regulation potential quantification submodule and a multi-time scale evaluation submodule.

[0018] Preferably, the regulation potential quantification submodule has a photovoltaic prediction and energy storage dynamic model,

[0019] The photovoltaic forecast: LSTM network predicts the output in the next 15 minutes and combines quantile regression to generate confidence intervals;

[0020] The energy storage dynamic model is based on RC equivalent circuit modeling, taking into account the SOC-efficiency curve and cycle aging coefficient;

[0021] The multi-time scale evaluation submodule has the following functions:

[0022] Short-time scale: Sensitivity analysis is used to calculate voltage / frequency regulation margin, i.e., at the second level;

[0023] Medium- to long-term scale: Generate a typical scenario tree based on Monte Carlo simulation, i.e. hourly level;

[0024] The dynamic assessment module inputs: standardized data stream, weather forecast, load history data;

[0025] The dynamic evaluation module outputs: an adjustment capability index matrix, which includes multi-dimensional indicators of power, voltage, and frequency.

[0026] Preferably, the collaborative optimization module realizes multi-objective collaborative decision-making to solve the problems of control lag and target conflict.

[0027] Preferably, the collaborative optimization module includes a target decomposition unit and a distributed solver.

[0028] The target decomposition unit maps the regulation capability, economy and equipment life into a Pareto frontier multi-objective function and generates a non-inferior solution set using the NSGA-III algorithm;

[0029] The distributed solver includes:

[0030] PV side: Construct the QP problem with the goal of minimizing power tracking deviation;

[0031] Energy storage side: Rolling optimization of charging and discharging strategies based on model predictive control;

[0032] Charging pile side: dynamic electricity price is used to smooth the load curve;

[0033] The collaborative optimization module inputs the regulation capability index matrix, real-time electricity price, and equipment status, and outputs the photovoltaic storage and charging collaborative control instructions, which include power setting value, charging and discharging plan, and electricity price strategy.

[0034] Preferably, the digital twin verification module verifies the feasibility of the control strategy online to form a closed-loop feedback.

[0035] Preferably, the digital twin verification module includes a digital twin and a strategy correction unit;

[0036] The digital twin: A hardware-in-the-loop simulation model is built based on the RT-LAB platform, including virtual devices such as photovoltaic inverters, energy storage BMS, and charging pile controllers;

[0037] Strategy correction unit: compares actual operation data with simulation results and dynamically adjusts evaluation model parameters;

[0038] The digital twin verification module inputs collaborative control instructions and real-time operation data, and outputs verification reports and model parameter corrections.

[0039] An evaluation method for a microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging is provided. The specific steps of the evaluation method are as follows:

[0040] S1: data flow logic;

[0041] The multi-source data fusion module provides standardized input for the dynamic evaluation module, eliminating the impact of data heterogeneity on the evaluation. The adjustment capability index output by the dynamic evaluation module serves as a constraint condition for the collaborative optimization module to ensure that the optimization target matches the actual capability. The control instructions generated by the collaborative optimization module must be simulated and tested by the digital twin verification module. After passing the verification, they are issued to the actual equipment for execution.

[0042] S2: control flow logic;

[0043] The digital twin verification module uses a feedback mechanism to modify the dynamic evaluation model parameters, forming a closed loop of "evaluation-optimization-verification-iteration";

[0044] When an extreme event is detected, the emergency mode of the collaborative optimization module is triggered, skipping the regular optimization process and directly calling the preset security strategy;

[0045] S3: time scale coupling;

[0046] Seconds: The sensitivity analysis results of the dynamic evaluation module directly drive the real-time power allocation of the collaborative optimization module. Minutes: The simulation results of the digital twin verification module are used to correct the medium- and long-term evaluation model.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] Hybrid spatiotemporal data fusion technology uses a three-level processing approach of "protocol conversion + spatiotemporal interpolation + delay compensation" to control the synchronization error of multi-source data within ±100ms.

[0049] The dynamic evaluation-optimization joint solution method iteratively solves the evaluation model (main problem) and the optimization model (sub-problem) based on Benders decomposition, reducing the computational complexity by 40%.

[0050] The event-driven rolling optimization mechanism defines the PV output fluctuation rate and load mutation rate as trigger thresholds, and only re-evaluates the affected subsystems to reduce the amount of calculation.

[0051] Closed-loop feedback: The digital twin verification module inputs the strategy correction value back into the dynamic evaluation module to achieve online update of model parameters.

[0052] Module decoupling design: Each module communicates through a standardized interface and supports independent upgrades (e.g., replacing the photovoltaic prediction algorithm does not affect collaborative optimization).

[0053] Closed-loop verification mechanism: Through digital twins, the entire process of "assessment-execution-feedback" can be verified to avoid the risks of aggressive strategies.

[0054] Dynamic adaptability: The event triggering mechanism ensures that the system maintains a high response speed in scenarios such as sudden changes in light intensity and surges in load. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a system logic block diagram of a microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to the present invention;

[0056] Figure 2 This is a workflow diagram of a microgrid regulation capability evaluation system based on the collaborative interaction of photovoltaic storage and charging according to the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0059] Example 1:

[0060] See also Figure 1-2 The present invention provides a technical solution: a microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging, comprising:

[0061] Multi-source data fusion module, dynamic evaluation module, collaborative optimization module, digital twin verification module and output control module, the signal output end of the multi-source data fusion module is connected to the dynamic evaluation module, the signal output end of the dynamic evaluation module is connected to the collaborative optimization module, the signal output end of the collaborative optimization module is connected to the digital twin verification module, and the signal output end of the digital twin verification module is connected to the output control module.

[0062] The multi-source data fusion module solves data heterogeneity and realizes unified access and preprocessing of real-time data. The multi-source data fusion module includes a protocol adaptation layer, a data cleaning unit, and a spatiotemporal alignment engine.

[0063] The protocol adaptation layer: supports Modbus / TCP, MQTT, and OPCUA conversion, and unifies the original data into JSON format;

[0064] Introduction to Modbus / TCP: Modbus / TCP is an Ethernet version based on the Modbus protocol. It encapsulates the Modbus protocol in the TCP / IP protocol to achieve remote communication between devices.

[0065] Compared with existing technical solutions, the Modbus / TCP involved in this application has the following characteristics:

[0066] Easy to use: The Modbus protocol itself has a concise structure and easy-to-understand instruction set, making device development and integration relatively easy.

[0067] Wide support: Due to the long history of the Modbus protocol, many industrial devices support the Modbus / TCP protocol and have high compatibility.

[0068] Good real-time performance: Suitable for industrial control scenarios that do not require extremely high real-time performance, and can meet the data interaction needs of most industrial equipment.

[0069] Application scenarios: Commonly used in industrial automation fields, such as communication between PLCs (programmable logic controllers), smart meters, inverters and other devices to achieve remote monitoring, data collection and control of equipment.

[0070] Introduction to MQTT (Message Queuing Telemetry Transport): MQTT is a lightweight, publish / subscribe-based message transmission protocol. It has the characteristics of low power consumption and low bandwidth usage, making it very suitable for use in resource-constrained devices and network environments.

[0071] Compared with existing technical solutions, the MQTT involved in this application has the following characteristics:

[0072] Asynchronous communication: Using the publish / subscribe model, the sender and receiver of the message do not need to interact directly, which decouples the communication between devices and improves the flexibility and scalability of the system.

[0073] Reliable transmission: supports message confirmation, retransmission and other mechanisms to ensure the reliability of messages during transmission.

[0074] Support multiple platforms: It can run on various operating systems and hardware platforms, including embedded devices, smart phones, servers, etc., facilitating interconnection between different types of devices.

[0075] Application scenarios: It is widely used in the field of Internet of Things (IoT), such as smart homes, smart cities, and industrial Internet of Things. It is used to connect a large number of sensors, actuators, and cloud platforms to achieve real-time data exchange and remote control between devices.

[0076] Introduction to OPCUA (Unified Architecture): OPCUA is an industrial communication protocol based on service-oriented architecture (SOA). It provides a unified, cross-platform communication method that can achieve seamless integration and interoperability between different manufacturers and different types of equipment.

[0077] Compared with the existing technical solutions, the OPC UA involved in this application has the following characteristics:

[0078] Platform independence: It adopts object-oriented design concept and describes devices and data through abstract information models, so that OPC UA can communicate between different operating systems and programming languages, and has strong platform independence.

[0079] High security: Supports multiple security mechanisms, such as authentication, encryption, and access control, to ensure data security and device security during communication.

[0080] Strong scalability: With a flexible architecture and rich feature set, new functions and services can be easily expanded to meet the needs of different industrial application scenarios.

[0081] Application scenarios: It has been widely used in industrial automation, intelligent manufacturing and other fields. It is particularly suitable for scenarios that require high interoperability and data security, such as automobile manufacturing, chemical industry, electric power and other industries. It is used to achieve factory-level equipment integration, data sharing and collaborative control.

[0082] JSON (JavaScript Object Notation) is a lightweight data exchange format that is easy for humans to read and write, and also easy for machines to parse and generate. The following is a detailed introduction to the JSON format:

[0083] Basic grammar rules

[0084] The data consists of key-value pairs: the key name in the key-value pair is written in front and wrapped in double quotes, the key and value are separated by a colon, and multiple key-value pairs are separated by commas.

[0085] Data uses curly braces to store objects: curly braces are used to represent an object, which can contain multiple key-value pairs.

[0086] Data is stored in arrays using square brackets: Square brackets are used to denote arrays. Arrays can contain multiple values, which can be different types of data such as strings, numbers, Booleans, objects, or other arrays.

[0087] Application Scenario

[0088] Web Services: JSON is widely used in data exchange between front-end and back-end. The front-end can send JSON-formatted data to the back-end API, which then processes and returns a JSON-formatted response.

[0089] Configuration files: Many applications use the JSON format to store configuration information because it is easy to read, write, and parse.

[0090] Data storage: Some databases and data storage systems support data storage in JSON format, which facilitates data management and query.

[0091] Comparison with other formats

[0092] Compared with XML, JSON is more concise, usually parses faster, and is more popular in web development; however, XML has a stricter syntax structure and better scalability, and is more commonly used in scenarios that require strict data validation and document specifications.

[0093] The data cleaning unit removes abnormal data based on sliding window mean filtering and isolation forest algorithm, and the window size of the sliding window mean is 10s;

[0094] The spatiotemporal alignment engine uses Newton interpolation to align data with different sampling frequencies and compensates for communication delays through Kalman filtering.

[0095] The multi-source data fusion module inputs: photovoltaic output, energy storage SOC, and charging pile power raw data, and outputs: standardized data stream with time synchronization and unified format.

[0096] The dynamic evaluation module quantifies the real-time regulation capability of the microgrid and solves the static model error problem. The dynamic evaluation module includes a regulation potential quantification submodule and a multi-time scale evaluation submodule.

[0097] The regulation potential quantification submodule has photovoltaic prediction and energy storage dynamic models,

[0098] The photovoltaic forecast: LSTM network predicts the output in the next 15 minutes and combines quantile regression to generate confidence intervals;

[0099] The energy storage dynamic model is based on RC equivalent circuit modeling, taking into account the SOC-efficiency curve and cycle aging coefficient;

[0100] The multi-time scale evaluation submodule has the following functions:

[0101] Short-time scale: Sensitivity analysis is used to calculate voltage / frequency regulation margin, i.e., at the second level;

[0102] Medium- to long-term scale: Generate a typical scenario tree based on Monte Carlo simulation, i.e. hourly level;

[0103] The dynamic assessment module inputs: standardized data stream, weather forecast, load history data;

[0104] The dynamic evaluation module outputs: an adjustment capability index matrix, which includes multi-dimensional indicators of power, voltage, and frequency.

[0105] The collaborative optimization module realizes multi-objective collaborative decision-making and solves the problems of control lag and target conflict.

[0106] The collaborative optimization module includes a target decomposition unit and a distributed solver.

[0107] The target decomposition unit maps the regulation capability, economy and equipment life into a Pareto frontier multi-objective function and generates a non-inferior solution set using the NSGA-III algorithm;

[0108] The distributed solver includes:

[0109] PV side: Construct the QP problem with the goal of minimizing power tracking deviation;

[0110] Energy storage side: Rolling optimization of charging and discharging strategies based on model predictive control;

[0111] Charging pile side: dynamic electricity price is used to smooth the load curve;

[0112] The collaborative optimization module inputs the regulation capability index matrix, real-time electricity price, and equipment status, and outputs the photovoltaic storage and charging collaborative control instructions, which include power setting value, charging and discharging plan, and electricity price strategy.

[0113] The digital twin verification module verifies the feasibility of the control strategy online to form a closed-loop feedback.

[0114] The digital twin verification module includes a digital twin and a strategy correction unit;

[0115] The digital twin: A hardware-in-the-loop simulation model is built based on the RT-LAB platform, including virtual devices such as photovoltaic inverters, energy storage BMS, and charging pile controllers;

[0116] Selecting modeling tools: RT-LAB supports a variety of modeling tools, such as MATLAB / Simulink, PSCAD, etc. Select the appropriate tool based on the system characteristics and personal familiarity.

[0117] Mathematical model creation: Use the selected tools to create a mathematical model of the system to accurately describe the system's dynamic characteristics. For example, in power system simulation, models of components such as generators, transformers, and lines are created.

[0118] Model packaging and configuration: Package the established model into a module suitable for use on the RT-LAB platform, and perform necessary configurations, such as sampling time and solver type.

[0119] RT-LAB platform configuration

[0120] Hardware connection: Connect the actual hardware to the real-time processor of the RT-LAB platform through the corresponding interface to ensure a stable and reliable connection.

[0121] Platform initialization: Start the RT-LAB software and perform initialization settings for the platform, including selecting a suitable real-time processor and configuring communication parameters.

[0122] Import model: Import the model created and packaged in the modeling tool into the RT-LAB platform.

[0123] Real-time optimization

[0124] Model segmentation and scheduling: Segment the imported model, allocate it to different processor cores, and set appropriate scheduling strategies to ensure real-time operation of the model.

[0125] Code generation and download: Use RT-LAB's code generation tools to convert the model into real-time executable code and download it to the real-time processor.

[0126] Hardware-in-the-loop testing

[0127] Signal mapping: Map the signals in the model with the input and output signals of the actual hardware to ensure correct data transmission.

[0128] Simulation run: Start hardware-in-the-loop simulation, monitor the running status of the model and the response of the hardware, and collect relevant data.

[0129] Fault injection and testing: During the simulation process, various faults (such as short circuit, open circuit, etc.) can be injected to test the performance and reliability of the hardware under abnormal conditions.

[0130] Result analysis and optimization

[0131] Data analysis: Analyze the data collected during the simulation process to evaluate whether the hardware performance indicators meet the design requirements.

[0132] Model modification: Based on the analysis results, the model is modified and optimized to improve the accuracy of the model and the reliability of the simulation.

[0133] Repeated testing: Repeated testing of the revised model is performed until satisfactory results are achieved.

[0134] Strategy correction unit: compares actual operation data with simulation results and dynamically adjusts evaluation model parameters;

[0135] The digital twin verification module inputs collaborative control instructions and real-time operation data, and outputs verification reports and model parameter corrections.

[0136] An evaluation method for a microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging is provided. The specific steps of the evaluation method are as follows:

[0137] S1: data flow logic;

[0138] The multi-source data fusion module provides standardized input for the dynamic evaluation module, eliminating the impact of data heterogeneity on the evaluation. The adjustment capability index output by the dynamic evaluation module serves as a constraint condition for the collaborative optimization module to ensure that the optimization target matches the actual capability. The control instructions generated by the collaborative optimization module must be simulated and tested by the digital twin verification module. After passing the verification, they are issued to the actual equipment for execution.

[0139] S2: control flow logic;

[0140] The digital twin verification module uses a feedback mechanism to modify the dynamic evaluation model parameters, forming a closed loop of "evaluation-optimization-verification-iteration";

[0141] When an extreme event is detected, the emergency mode of the collaborative optimization module is triggered, skipping the regular optimization process and directly calling the preset security strategy;

[0142] S3: time scale coupling;

[0143] Seconds: The sensitivity analysis results of the dynamic evaluation module directly drive the real-time power allocation of the collaborative optimization module. Minutes: The simulation results of the digital twin verification module are used to correct the medium- and long-term evaluation model.

[0144] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the appended claims rather than the above description. Therefore, it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any figure signs in the claims should not be regarded as limiting the claims involved.

[0145] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging, characterized by: include: Multi-source data fusion module, dynamic evaluation module, collaborative optimization module, digital twin verification module and output control module; Among them, the signal output end of the multi-source data fusion module is connected to the dynamic evaluation module, the signal output end of the dynamic evaluation module is connected to the collaborative optimization module, the signal output end of the collaborative optimization module is connected to the digital twin verification module, and the signal output end of the digital twin verification module is connected to the output control module.

2. The microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to claim 1 is characterized by: The multi-source data fusion module solves data heterogeneity and realizes unified access and preprocessing of real-time data. The multi-source data fusion module includes a protocol adaptation layer, a data cleaning unit, and a spatiotemporal alignment engine.

3. The microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to claim 2 is characterized by: The protocol adaptation layer: supports Modbus / TCP, MQTT, and OPCUA conversion, and unifies the original data into JSON format; The data cleaning unit removes abnormal data based on sliding window mean filtering and isolation forest algorithm, and the window size of the sliding window mean is 10s; The spatiotemporal alignment engine uses Newton interpolation to align data with different sampling frequencies and compensates for communication delays through Kalman filtering. The multi-source data fusion module inputs: photovoltaic output, energy storage SOC, and charging pile power raw data, and outputs: standardized data stream with time synchronization and unified format.

4. The microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to claim 1 is characterized by: The dynamic evaluation module quantifies the real-time regulation capability of the microgrid and solves the static model error problem. The dynamic evaluation module includes a regulation potential quantification submodule and a multi-time scale evaluation submodule.

5. The microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to claim 4 is characterized by: The regulation potential quantification submodule has photovoltaic prediction and energy storage dynamic models, The photovoltaic forecast: LSTM network predicts the output in the next 15 minutes and combines quantile regression to generate confidence intervals; The energy storage dynamic model is based on RC equivalent circuit modeling, taking into account the SOC-efficiency curve and cycle aging coefficient; The multi-time scale evaluation submodule has the following functions: Short-time scale: Sensitivity analysis is used to calculate voltage / frequency regulation margin, i.e., at the second level; Medium- to long-term scale: Generate a typical scenario tree based on Monte Carlo simulation, i.e. hourly level; The dynamic assessment module inputs: standardized data stream, weather forecast, load history data; The dynamic evaluation module outputs: an adjustment capability index matrix, which includes multi-dimensional indicators of power, voltage, and frequency.

6. The microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to claim 1 is characterized by: The collaborative optimization module realizes multi-objective collaborative decision-making and solves the problems of control lag and target conflict.

7. The microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to claim 1 is characterized by: The collaborative optimization module includes a target decomposition unit and a distributed solver. The target decomposition unit maps the regulation capability, economy and equipment life into a Pareto frontier multi-objective function and generates a non-inferior solution set using the NSGA-III algorithm; The distributed solver includes: PV side: Construct the QP problem with the goal of minimizing power tracking deviation; Energy storage side: Rolling optimization of charging and discharging strategies based on model predictive control; Charging pile side: Dynamic electricity prices are used to smooth the load curve.

8. The microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to claim 7 is characterized by: The collaborative optimization module inputs the regulation capability index matrix, real-time electricity price, and equipment status, and outputs the photovoltaic storage and charging collaborative control instructions, which include power setting value, charging and discharging plan, and electricity price strategy.

9. The microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to claim 1 is characterized by: The digital twin verification module verifies the feasibility of the control strategy online to form a closed-loop feedback.

10. The microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to claim 9 is characterized by: The digital twin verification module includes a digital twin and a strategy correction unit. The digital twin: a hardware-in-the-loop simulation model is built based on the RT-LAB platform, including virtual devices such as photovoltaic inverters, energy storage BMS, and charging pile controllers. Strategy correction unit: compares actual operation data with simulation results and dynamically adjusts evaluation model parameters.

11. The microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to claim 10 is characterized by: The digital twin verification module inputs collaborative control instructions and real-time operation data, and outputs verification reports and model parameter corrections.

12. A method for evaluating a microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging, characterized by: Based on the microgrid regulation capability evaluation system based on the synergistic interaction of photovoltaic storage and charging according to any one of claims 1 to 11, the specific steps of the evaluation method are as follows: S1: data flow logic; The multi-source data fusion module provides standardized input for the dynamic evaluation module, eliminating the impact of data heterogeneity on the evaluation. The adjustment capability index output by the dynamic evaluation module serves as a constraint condition for the collaborative optimization module to ensure that the optimization target matches the actual capability. The control instructions generated by the collaborative optimization module must be simulated and tested by the digital twin verification module. After passing the verification, they are issued to the actual equipment for execution. S2: control flow logic; The digital twin verification module uses a feedback mechanism to modify the dynamic evaluation model parameters, forming a closed loop of "evaluation-optimization-verification-iteration"; When an extreme event is detected, the emergency mode of the collaborative optimization module is triggered, skipping the regular optimization process and directly calling the preset security strategy; S3: time scale coupling; Seconds: The sensitivity analysis results of the dynamic evaluation module directly drive the real-time power allocation of the collaborative optimization module. Minutes: The simulation results of the digital twin verification module are used to correct the medium- and long-term evaluation model.

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