Source network load storage collaborative management and control method and system

Abstract

The invention relates to the technical field of power system control, in particular to a source network load storage collaborative management and control method and system. According to the technical scheme, the source network load storage collaborative management and control system comprises a panoramic perception and digital twinning module, a collaborative optimization decision module and a closed-loop control and execution management module; according to the method, a closed-loop self-adaptive correction mechanism based on digital twinning and model prediction control is introduced, the execution effect of an instruction, the actual response of a controlled object and latest ultra-short-term prediction data are fed back to a digital twinning model in real time, the model serves as a system synchronously evolved and continued with a physical power grid, and the real-time performance of the model is improved. The method does not depend on a preset strategy any more, instant response can be made to various disturbances inside and outside, and the effects of coping with uncertainty and maintaining safe and stable operation of the system are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power system control technology, and in particular to a method and system for coordinated management and control of power generation, grid, load and storage. Background Technology

[0002] The source-grid-load-storage coordinated management and control system is an intelligent energy management platform that integrates the generation side, power grid, power consumption side and energy storage system. Through real-time monitoring, dynamic coordination and optimized scheduling, it realizes the efficient coordinated operation of all links of source, grid, load and storage.

[0003] Existing source-grid-load-storage coordinated management and control methods and systems suffer from static control strategies and a lack of adaptive capabilities. They cannot dynamically adjust according to the real-time operating status of the power grid. In the power system, when renewable energy output fluctuates drastically, load changes unexpectedly, or equipment fails suddenly, the pre-defined static strategies may become inapplicable, leading to new safety risks. At the same time, the system lacks a closed-loop feedback mechanism to continuously observe the control effect, identify model deviations in a timely manner, and quickly re-optimize decisions, resulting in rigid control behavior that is difficult to adapt to the highly uncertain operating environment of the power system under a high proportion of renewable energy access.

[0004] To address the aforementioned issues, this solution introduces a closed-loop adaptive correction mechanism based on digital twin and model predictive control. This mechanism feeds back the execution effect of commands, the actual response of the controlled object, and the latest ultra-short-term forecast data to the digital twin model in real time. As a system that evolves synchronously and continuously with the physical power grid, this model no longer relies on pre-set strategies and can respond instantly to various internal and external disturbances, significantly improving the system's ability to cope with uncertainties and maintain safe and stable operation. Summary of the Invention

[0005] To overcome the problems of static control strategies and lack of adaptive capabilities in existing source-grid-load-storage coordinated management and control methods and systems, which cannot be dynamically adjusted according to the real-time operating status of the power grid, the pre-defined static strategies may become inapplicable when renewable energy output fluctuates drastically, load changes unexpectedly, or equipment fails suddenly in the power system, leading to new safety risks. At the same time, the system lacks a closed-loop feedback mechanism to continuously observe the control effect, identify model deviations in a timely manner, and quickly re-optimize decisions, resulting in rigid control behavior and difficulty in adapting to the highly uncertain operating environment of the power system under the high proportion of renewable energy access.

[0006] The technical solution of this invention is: a source-grid-load-storage coordinated management and control method, comprising the following steps: S11: Construct a unified real-time database covering the entire domain, including sources, networks, loads, and storage; S12: Achieving accurate perception, safety assessment, and risk warning of power grid conditions across multiple time and space based on digital twin technology; S13: Generate source, grid, load, and storage coordinated operation strategies and control commands through a multi-timescale optimization model; S14: Decompose system-level instructions and securely distribute them to all controllable resources for rapid and precise coordinated control; S15: Form a closed loop of "monitoring-evaluation-optimization-correction" to dynamically adjust strategies and continuously optimize the system.

[0007] As a preferred method, the following steps are included when collecting and standardizing heterogeneous data from multiple sources across the entire domain: S21: At power plant outlets, main grid hub nodes, important load access points, and energy storage power station grid connection points, deploy and connect synchronous phasor measurement units capable of acquiring voltage, current, and frequency phasors at millisecond speeds; S22: Through the power plant monitoring system, real-time data on the actual output, active / reactive power limits, ramp rate, start-stop status, and predicted wind and solar power of various generator units are collected to form a panoramic view of power generation capacity; S23: Obtain real-time topology of the power grid, status of circuit breakers / disconnectors, and active power flow, reactive power flow, voltage amplitude, and load factor data for all transmission lines and transformers from the energy management system; S24: Through advanced measurement systems and load control terminals, collect and summarize interruptible power, transferable power, response time, and comfort constraint parameters of flexible loads from large industrial users, commercial buildings, electric vehicle charging stations, and smart homes; S25: Obtain the current state of charge, rated power, available charge / discharge capacity, health status, and charge / discharge efficiency curves of each energy storage unit in real time from the battery management system and energy storage converter monitoring system; S26: Apply a rule engine and state estimation algorithm to the collected raw data to identify and remove obviously erroneous bad data, and repair data loss caused by communication interruption by interpolating historical data or fitting data from neighboring nodes; S27: Data with different collection frequencies and time scales are unified to the core calculation cycle set by the control system through interpolation or aggregation algorithms, and all data are mapped to a common CIM / E model to form a standardized object-attribute-measurement database.

[0008] Preferably, when performing multi-temporal and spatiotemporal state perception and modeling of a system based on digital twins, the following steps are included: S31: Using the wide-area measurement system data provided by the synchronous phasor measurement unit, combined with the state estimation results, the generator inertial constant and load dynamic characteristic model parameters are identified online, and the digital twin model parameter library is updated; S32: Using the fused data as the measurement input, the weighted least squares method or robust estimation method is used to estimate the state of the entire network, calculate the voltage amplitude and phase angle of each node that is closest to the actual operating state, and eliminate data noise and error; S33: Based on the current operating mode, automatically iterate through all fault scenarios where major components are switched on or off, quickly calculate the power flow distribution under each scenario, and filter out the weak links that cause equipment overload or voltage exceeding limits; S34: Using a linearized system model, analyze the oscillation modes and damping characteristics of the system under small disturbances; simultaneously, assess the distance between the current operating point and the voltage stability critical point using the continuous power flow method or sensitivity analysis method. S35: Apply time series analysis or deep learning network algorithms to make rolling forecasts of wind and solar power generation and regional load demand for the next 15 minutes to 4 hours, generating probabilistic forecast intervals to quantify uncertainty; S36: Based on the individual characteristics and constraints of flexible loads and energy storage, aggregation theory is used to aggregate geographically dispersed and diverse resources into a virtual power plant model, characterizing its overall power regulation range, response speed, and duration; S37: Based on comprehensive safety analysis, stability assessment and prediction results, quantitatively calculate the system's current and near-future comprehensive risk indicators from the dimensions of equipment overload, voltage exceeding limits, frequency stability and renewable energy fluctuations.

[0009] Preferably, the following steps are included when performing multi-timescale collaborative optimization decision-making and strategy generation: S41: Establish a multi-objective optimization function that minimizes the total system operating cost, grid loss, renewable energy absorption rate, and voltage deviation, taking into account the physical and operational constraints of all aspects of the source, grid, load, and storage systems; S42: During the day-ahead phase, based on load and renewable energy forecasts, hybrid integer programming or heuristic algorithms are used to optimize and formulate routine unit start-up and shutdown plans, inter-regional electricity trading plans, and energy storage charging and discharging plans. These plans are adjusted and optimized every 15 minutes throughout the day. S43: Embed AC or DC power flow constraints in the optimization model, and solve for the marginal electricity price at each node that reflects the grid congestion and power supply cost through optimal power flow calculation; S44: Based on the marginal electricity price at the node or the system regulation demand, design time-of-use pricing, real-time pricing, or direct load control signals, and generate dispatch sequences and power adjustment target values ​​for different types of flexible loads; S45: Taking into account multiple application scenarios such as electricity price arbitrage, smoothing fluctuations, and frequency regulation reserves, and balancing energy storage lifespan loss and system benefits, the charging and discharging power curves and operating mode switching logic of energy storage power stations are dynamically optimized; S46: Based on the voltage stability assessment results, coordinate and optimize the transformer tap position, parallel capacitor bank switching, distributed power source reactive power output, and static var compensator output to form a reactive power resource scheduling strategy to maintain voltage stability; S47: For potential risks identified by safety analysis, pre-calculate and generate a variety of preventive control strategies, including adjusting power generation output, load shedding, and emergency energy storage support, and set trigger thresholds and store them in the strategy library for later use.

[0010] Preferably, the following steps are included when issuing hierarchical partition control commands and dynamically allocating resources: S51: Based on the aggregation model and resource registration information, the system-level power and voltage optimization targets are precisely decomposed into specific power setpoints or state switching commands for individual generator sets, energy storage units, and flexible load groups; S52: Utilizing a dedicated power wireless network or fiber optic network, and employing IEC 60870-5-104, IEC 61850, or MQTT protocols, control commands are encrypted and then sent to monitoring systems, intelligent gateways, or terminal controllers at various plants and stations. S53: Based on regional control errors, the regional dispatch center issues adjustment commands ranging from seconds to minutes to the frequency regulation units through the automatic generation control system; simultaneously, it mobilizes energy storage and fast load resources to participate in primary frequency regulation. S54: The demand-side management platform sends price signals or direct control commands to registered flexible load users, and the user-side intelligent energy management system or controller automatically executes load interruption, shifting, or reduction operations accordingly; S55: The central controller dynamically allocates total charging and discharging power commands among multiple energy storage stations based on the real-time state of charge, power capacity, and geographical location of each energy storage station, according to an equal incremental rate or optimization algorithm, to avoid overcharging and over-discharging of individual units; S56: Issue active power limit instructions to the monitoring systems of wind farms and photovoltaic power stations to participate in peak shaving, and simultaneously issue reactive power or voltage control instructions to enable them to participate in the voltage regulation of the local power grid; S57: In the distribution network, active power, reactive power or voltage reference commands are issued to power electronic equipment to perform precise power flow control and eliminate branch overload and voltage over-limit.

[0011] Preferably, the following steps are included when performing closed-loop execution monitoring and adaptive rolling optimization correction: S61: After the command is issued, continuously compare the differences between the actual output, power or status data uploaded by each controlled unit and the target command, and calculate the command execution deviation and response delay time; S62: After the execution of control actions, comprehensively analyze the changing trends of operating indicators such as power flow at key sections of the system, node voltage, or system frequency, and evaluate the actual effect of the strategy on eliminating over-limits, smoothing fluctuations, and improving stability; S63: Continuously compare actual wind / solar power output with ultra-short-term forecasts, and actual load values ​​with forecasts, calculate forecast errors, and analyze the statistical characteristics and spatiotemporal distribution patterns of these errors; S64: Feed the actual response data back to the digital twin system to drive the model to perform simulation replay, analyze the reasons why the control strategy failed to achieve the expected results, and identify the differences between the model and reality; S65: Using the current system state as the initial value and combining the latest ultra-short-term forecast data, a new round of collaborative optimization calculation is initiated in each control cycle to regenerate the optimized control sequence for the next few cycles; S66: Based on the actual charge / discharge amount and state of charge change of the energy storage unit, reverse-calibrate the capacity or efficiency parameters in its equivalent model; S67: Based on changes in actual control effectiveness and system operation mode, periodically or trigger-based verification and updates of preventive control strategies stored in the strategy library.

[0012] A source-grid-load-storage coordinated management and control system comprises the following modules: Panoramic perception and digital twin module: used to collect, fuse, and clean real-time operational data across the entire domain, and to build a digital twin model that evolves synchronously with the physical power grid; Collaborative optimization decision-making module: Based on the system status and prediction information provided by the panoramic perception module, it generates a set of collaborative control strategies covering the four links of source, network, load and storage by solving multi-objective and multi-constraint optimization models. Closed-loop control and execution management module: This module is responsible for decomposing the strategy instructions generated by the optimization decision module and distributing them to each execution terminal, monitoring the execution status of the instructions, and making adaptive adjustments based on feedback.

[0013] As a preferred embodiment, the panoramic perception and digital twin module includes: A11: Data Acquisition and Fusion Unit, including a data acquisition gateway, a time-series database, and a data fusion server, is used to collect multi-source heterogeneous raw operational data from terminal devices in the source, network, load, and storage stages, and process it to form a system-level unified time-series database; A12: Model Management and Simulation Calculation Unit, including model server, high-performance computing cluster and power system analysis software package, used to build and maintain digital twin model of power grid, and receive fused real-time data to drive model operation; A13: Status Assessment and Prediction Early Warning Unit, including risk assessment server, prediction engine server and visualization alarm workstation, is used to assess the overall security and stability of the system and operational risks based on simulation calculation results and real-time data of digital twin model, and to make ultra-short-term predictions of key variables.

[0014] As a preferred option, the collaborative optimization decision-making module includes: A21: Optimization computing engine unit, including mathematical programming solver, heuristic algorithm computing server and optimization task scheduler, is used to solve large-scale, nonlinear source-network-load-storage collaborative optimization problems, and calculate the system operation mode and resource allocation scheme with the lowest total cost and the safest. A22: Policy generation and management unit, including policy formatting server, policy database and policy verification software, used to transform the mathematical results calculated by the optimization engine into specific, executable control policy instructions; A23: Market and Electricity Price Decision Unit, including LMP computing server, demand response management platform and market clearing engine, is specifically responsible for connecting with the electricity market, calculating node marginal electricity prices, designing demand-side response schemes, and generating corresponding market clearing and settlement signals.

[0015] As a preferred embodiment, the closed-loop control and execution management module includes: A31: Command distribution and communication unit, including command gateway server, security encryption device and message queue middleware, used to receive upper-layer policies, parse and decompose them into specific action commands for individual controllable devices; A32: Execution monitoring and verification unit, including data acquisition and monitoring control system, performance evaluation server and alarm management server, used to receive feedback information uploaded by each execution terminal in real time, compare whether the control commands are consistent with the actual actions of the equipment, and calculate the command execution rate and response delay; A33: Adaptive Correction and Rolling Optimization Unit, including Model Predictive Controller, Feedback Data Interface Server and Rolling Optimization Trigger, is used to dynamically trigger a new round of optimization calculations based on feedback from the execution monitoring unit and the latest system state perception data, and to adjust and correct subsequent control strategies in real time.

[0016] The beneficial effects of this invention are: Existing source-grid-load-storage coordinated management and control methods and systems suffer from static control strategies and a lack of adaptive capabilities. They cannot dynamically adjust according to the real-time operating status of the power grid. In the power system, when renewable energy output fluctuates drastically, load changes unexpectedly, or equipment fails suddenly, pre-defined static strategies may become inapplicable, leading to new safety risks. At the same time, the system lacks a closed-loop feedback mechanism for continuously observing control effects, timely identifying model deviations, and quickly re-optimizing decisions, resulting in rigid control behavior that is difficult to adapt to the highly uncertain operating environment of the power system under high-proportion renewable energy access. This solution introduces a closed-loop adaptive correction mechanism based on digital twin and model predictive control. It feeds back the execution effect of commands, the actual response of the controlled object, and the latest ultra-short-term forecast data to the digital twin model in real time. As a system that evolves synchronously and continuously with the physical power grid, this model no longer relies on pre-set strategies and can react instantly to various internal and external disturbances, significantly improving the system's ability to cope with uncertainty and maintain safe and stable operation. Attached Figure Description

[0017] Figure 1 The diagram shown is a schematic flowchart of a source-grid-load-storage coordinated management and control method according to the present invention. Figure 2 The diagram illustrates the process of multi-source heterogeneous data acquisition and standardized fusion across the entire domain in a source-grid-load-storage collaborative management and control method according to the present invention. Figure 3 The diagram illustrates the multi-temporal and spatial state perception and modeling process of a source-grid-load-storage collaborative management and control method based on digital twins, as presented in this invention. Figure 4 The diagram shown is a schematic of the multi-timescale collaborative optimization decision-making and strategy generation process of a source-grid-load-storage collaborative management and control method according to the present invention. Figure 5 The diagram shown is a schematic flowchart of a source-grid-load-storage collaborative management and control system according to the present invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Please see Figure 1-4 This invention provides an embodiment: a source-grid-load-storage coordinated management and control method, comprising the following steps: S11: Construct a unified real-time database covering the entire domain, including sources, networks, loads, and storage; S12: Achieving accurate perception, safety assessment, and risk warning of power grid conditions across multiple time and space based on digital twin technology; S13: Generate source, grid, load, and storage coordinated operation strategies and control commands through a multi-timescale optimization model; S14: Decompose system-level instructions and securely distribute them to all controllable resources for rapid and precise coordinated control; S15: Form a closed loop of "monitoring-evaluation-optimization-correction" to dynamically adjust strategies and continuously optimize the system.

[0020] As a preferred method, the following steps are included when collecting and standardizing heterogeneous data from multiple sources across the entire domain: S21: At power plant outlets, main grid hub nodes, important load access points, and energy storage power station grid connection points, deploy and connect synchronous phasor measurement units capable of acquiring voltage, current, and frequency phasors at millisecond speeds; S22: Through the power plant monitoring system, real-time data on the actual output, active / reactive power limits, ramp rate, start-stop status, and predicted wind and solar power of various generator units are collected to form a panoramic view of power generation capacity; S23: Obtain real-time topology of the power grid, status of circuit breakers / disconnectors, and active power flow, reactive power flow, voltage amplitude, and load factor data for all transmission lines and transformers from the energy management system; S24: Through advanced measurement systems and load control terminals, collect and summarize interruptible power, transferable power, response time, and comfort constraint parameters of flexible loads from large industrial users, commercial buildings, electric vehicle charging stations, and smart homes; S25: Obtain the current state of charge, rated power, available charge / discharge capacity, health status, and charge / discharge efficiency curves of each energy storage unit in real time from the battery management system and energy storage converter monitoring system; S26: Apply a rule engine and state estimation algorithm to the collected raw data to identify and remove obviously erroneous bad data, and repair data loss caused by communication interruption by interpolating historical data or fitting data from neighboring nodes; S27: Data with different collection frequencies and time scales are unified to the core calculation cycle set by the control system through interpolation or aggregation algorithms, and all data are mapped to a common CIM / E model to form a standardized object-attribute-measurement database.

[0021] Preferably, when performing multi-temporal and spatiotemporal state perception and modeling of a system based on digital twins, the following steps are included: S31: Using the wide-area measurement system data provided by the synchronous phasor measurement unit, combined with the state estimation results, the generator inertial constant and load dynamic characteristic model parameters are identified online, and the digital twin model parameter library is updated; S32: Using the fused data as the measurement input, the weighted least squares method or robust estimation method is used to estimate the state of the entire network, calculate the voltage amplitude and phase angle of each node that is closest to the actual operating state, and eliminate data noise and error; S33: Based on the current operating mode, automatically iterate through all fault scenarios where major components are switched on or off, quickly calculate the power flow distribution under each scenario, and filter out the weak links that cause equipment overload or voltage exceeding limits; S34: Using a linearized system model, analyze the oscillation modes and damping characteristics of the system under small disturbances; simultaneously, assess the distance between the current operating point and the voltage stability critical point using the continuous power flow method or sensitivity analysis method. S35: Apply time series analysis or deep learning network algorithms to make rolling forecasts of wind and solar power generation and regional load demand for the next 15 minutes to 4 hours, generating probabilistic forecast intervals to quantify uncertainty; S36: Based on the individual characteristics and constraints of flexible loads and energy storage, aggregation theory is used to aggregate geographically dispersed and diverse resources into a virtual power plant model, characterizing its overall power regulation range, response speed, and duration; S37: Based on comprehensive safety analysis, stability assessment and prediction results, quantitatively calculate the system's current and near-future comprehensive risk indicators from the dimensions of equipment overload, voltage exceeding limits, frequency stability and renewable energy fluctuations.

[0022] Preferably, the following steps are included when performing multi-timescale collaborative optimization decision-making and strategy generation: S41: Establish a multi-objective optimization function that minimizes the total system operating cost, grid loss, renewable energy absorption rate, and voltage deviation, taking into account the physical and operational constraints of all aspects of the source, grid, load, and storage systems; S42: During the day-ahead phase, based on load and renewable energy forecasts, hybrid integer programming or heuristic algorithms are used to optimize and formulate routine unit start-up and shutdown plans, inter-regional electricity trading plans, and energy storage charging and discharging plans. These plans are adjusted and optimized every 15 minutes throughout the day. S43: Embed AC or DC power flow constraints in the optimization model, and solve for the marginal electricity price at each node that reflects the grid congestion and power supply cost through optimal power flow calculation; S44: Based on the marginal electricity price at the node or the system regulation demand, design time-of-use pricing, real-time pricing, or direct load control signals, and generate dispatch sequences and power adjustment target values ​​for different types of flexible loads; S45: Taking into account multiple application scenarios such as electricity price arbitrage, smoothing fluctuations, and frequency regulation reserves, and balancing energy storage lifespan loss and system benefits, the charging and discharging power curves and operating mode switching logic of energy storage power stations are dynamically optimized; S46: Based on the voltage stability assessment results, coordinate and optimize the transformer tap position, parallel capacitor bank switching, distributed power source reactive power output, and static var compensator output to form a reactive power resource scheduling strategy to maintain voltage stability; S47: For potential risks identified by safety analysis, pre-calculate and generate a variety of preventive control strategies, including adjusting power generation output, load shedding, and emergency energy storage support, and set trigger thresholds and store them in the strategy library for later use.

[0023] Preferably, the following steps are included when issuing hierarchical partition control commands and dynamically allocating resources: S51: Based on the aggregation model and resource registration information, the system-level power and voltage optimization targets are precisely decomposed into specific power setpoints or state switching commands for individual generator sets, energy storage units, and flexible load groups; S52: Utilizing a dedicated power wireless network or fiber optic network, and employing IEC 60870-5-104, IEC 61850, or MQTT protocols, control commands are encrypted and then sent to monitoring systems, intelligent gateways, or terminal controllers at various plants and stations. S53: Based on regional control errors, the regional dispatch center issues adjustment commands ranging from seconds to minutes to the frequency regulation units through the automatic generation control system; simultaneously, it mobilizes energy storage and fast load resources to participate in primary frequency regulation. S54: The demand-side management platform sends price signals or direct control commands to registered flexible load users, and the user-side intelligent energy management system or controller automatically executes load interruption, shifting, or reduction operations accordingly; S55: The central controller dynamically allocates total charging and discharging power commands among multiple energy storage stations based on the real-time state of charge, power capacity, and geographical location of each energy storage station, according to an equal incremental rate or optimization algorithm, to avoid overcharging and over-discharging of individual units; S56: Issue active power limit instructions to the monitoring systems of wind farms and photovoltaic power stations to participate in peak shaving, and simultaneously issue reactive power or voltage control instructions to enable them to participate in the voltage regulation of the local power grid; S57: In the distribution network, active power, reactive power or voltage reference commands are issued to power electronic equipment to perform precise power flow control and eliminate branch overload and voltage over-limit.

[0024] Preferably, the following steps are included when performing closed-loop execution monitoring and adaptive rolling optimization correction: S61: After the command is issued, continuously compare the differences between the actual output, power or status data uploaded by each controlled unit and the target command, and calculate the command execution deviation and response delay time; S62: After the execution of control actions, comprehensively analyze the changing trends of operating indicators such as power flow at key sections of the system, node voltage, or system frequency, and evaluate the actual effect of the strategy on eliminating over-limits, smoothing fluctuations, and improving stability; S63: Continuously compare actual wind / solar power output with ultra-short-term forecasts, and actual load values ​​with forecasts, calculate forecast errors, and analyze the statistical characteristics and spatiotemporal distribution patterns of these errors; S64: Feed the actual response data back to the digital twin system to drive the model to perform simulation replay, analyze the reasons why the control strategy failed to achieve the expected results, and identify the differences between the model and reality; S65: Using the current system state as the initial value and combining the latest ultra-short-term forecast data, a new round of collaborative optimization calculation is initiated in each control cycle to regenerate the optimized control sequence for the next few cycles; S66: Based on the actual charge / discharge amount and state of charge change of the energy storage unit, reverse-calibrate the capacity or efficiency parameters in its equivalent model; S67: Based on changes in actual control effectiveness and system operation mode, periodically or trigger-based verification and updates of preventive control strategies stored in the strategy library.

[0025] Please see Figure 5 In this embodiment, a source-grid-load-storage coordinated management and control system comprises the following modules: Panoramic perception and digital twin module: used to collect, fuse, and clean real-time operational data across the entire domain, and to build a digital twin model that evolves synchronously with the physical power grid; Collaborative optimization decision-making module: Based on the system status and prediction information provided by the panoramic perception module, it generates a set of collaborative control strategies covering the four links of source, network, load and storage by solving multi-objective and multi-constraint optimization models. Closed-loop control and execution management module: This module is responsible for decomposing the strategy instructions generated by the optimization decision module and distributing them to each execution terminal, monitoring the execution status of the instructions, and making adaptive adjustments based on feedback.

[0026] As a preferred embodiment, the panoramic perception and digital twin module includes: A11: Data Acquisition and Fusion Unit, including a data acquisition gateway, a time-series database, and a data fusion server, is used to collect multi-source heterogeneous raw operational data from terminal devices in the source, network, load, and storage stages, and process it to form a system-level unified time-series database; A12: Model Management and Simulation Calculation Unit, including model server, high-performance computing cluster and power system analysis software package, used to build and maintain digital twin model of power grid, and receive fused real-time data to drive model operation; A13: Status Assessment and Prediction Early Warning Unit, including risk assessment server, prediction engine server and visualization alarm workstation, is used to assess the overall security and stability of the system and operational risks based on simulation calculation results and real-time data of digital twin model, and to make ultra-short-term predictions of key variables.

[0027] As a preferred option, the collaborative optimization decision-making module includes: A21: Optimization computing engine unit, including mathematical programming solver, heuristic algorithm computing server and optimization task scheduler, is used to solve large-scale, nonlinear source-network-load-storage collaborative optimization problems, and calculate the system operation mode and resource allocation scheme with the lowest total cost and the safest. A22: Policy generation and management unit, including policy formatting server, policy database and policy verification software, used to transform the mathematical results calculated by the optimization engine into specific, executable control policy instructions; A23: Market and Electricity Price Decision Unit, including LMP computing server, demand response management platform and market clearing engine, is specifically responsible for connecting with the electricity market, calculating node marginal electricity prices, designing demand-side response schemes, and generating corresponding market clearing and settlement signals.

[0028] As a preferred embodiment, the closed-loop control and execution management module includes: A31: Command distribution and communication unit, including command gateway server, security encryption device and message queue middleware, used to receive upper-layer policies, parse and decompose them into specific action commands for individual controllable devices; A32: Execution monitoring and verification unit, including data acquisition and monitoring control system, performance evaluation server and alarm management server, used to receive feedback information uploaded by each execution terminal in real time, compare whether the control commands are consistent with the actual actions of the equipment, and calculate the command execution rate and response delay; A33: Adaptive Correction and Rolling Optimization Unit, including Model Predictive Controller, Feedback Data Interface Server and Rolling Optimization Trigger, is used to dynamically trigger a new round of optimization calculations based on feedback from the execution monitoring unit and the latest system state perception data, and to adjust and correct subsequent control strategies in real time.

[0029] Example 1 Background: This project addresses the challenges of high-proportion renewable energy consumption and stable system operation in a regional power grid. This grid relies heavily on large-scale offshore wind power and distributed photovoltaic power, with load centers encompassing high-end manufacturing parks and smart city complexes. It also includes multiple large-scale electrochemical energy storage power stations. Before implementing this system, the grid faced the following severe challenges: First, the dramatic fluctuations in offshore wind power output, coupled with peak loads, often resulted in heavy loads on the main transmission lines, forcing wind farms to curtail wind power. Second, traditional dispatching methods, relying on source-grid coordination, could not effectively mobilize the flexibility of load-side and energy storage resources for real-time balancing. Third, static safety control strategies were ill-equipped to handle rapid changes in grid operation, posing potential safety risks. To address these issues, a management and control system capable of deep coordination and adaptive optimization across all aspects of the power grid—source, grid, load, and storage—was needed.

[0030] Implementation steps: S71: Within the implementation area, existing synchronous phasor measurement units will be fully installed or utilized at all offshore wind farm grid connection points, onshore hub substations, major manufacturing user booster stations, and energy storage power station outlets to form a wide-area measurement network. Simultaneously, the data interfaces of each power plant monitoring system, grid energy management system, user-side advanced measurement system, and battery management system will be standardized. All acquisition terminals will continuously upload millisecond-level grid dynamic data, minute-level wind and solar power prediction data, and second-level equipment status data and load characteristic parameters to the regional data center via a dedicated power communication network. The data fusion server will run a rule engine to perform online cleaning and status estimation of massive amounts of raw data, repairing data gaps caused by communication delays. Finally, all processed, time-stamped data will be mapped to a network-wide model built based on the CIM / E standard, forming a real-time updated, highly consistent object-attribute-measurement database, providing clean and reliable data fuel for subsequent advanced applications. S72: Based on a unified database, the model management server loads detailed equipment parameters and topology relationships of the regional power grid, constructs an initial digital twin model, and then the high-performance computing cluster is activated. Using the fused real-time data as measurement input, it performs weighted least squares state estimation to accurately calculate the voltage and phase angle of each node in the entire network, reproducing the actual operating status of the power grid. On this basis, the system automatically performs N-1 static security analysis, scans the power flow transfer situation after all critical lines and transformer faults, and identifies overload risk points. At the same time, it uses wide-area measurement system data to identify the equivalent inertial constant of wind turbines and the parameters of the regional load dynamic model online, updating the twin model parameter library to make it closer to reality. The prediction engine server performs ultra-short-term predictions of wind power, photovoltaics, and regional total load for the next four hours and generates probabilistic prediction results. Combining all these analysis results, the system generates a panoramic assessment report covering equipment safety, static stability, and operational risks, and displays it to the dispatcher through a visualization workstation. S73: The collaborative optimization decision-making module, based on the current status and prediction information provided by the digital twin, initiates the optimization calculation engine. During the day-ahead phase, with the primary objectives of minimizing total system operating costs and maximizing renewable energy consumption, it comprehensively considers unit operation constraints, network power flow constraints, energy storage charging / discharging frequency limits, and flexible load response capabilities. Using a mixed-integer programming algorithm, it formulates the regular unit start-up and shutdown plan for the next 24 hours, the trading plan with neighboring networks, and the charging / discharging plan for each energy storage station. Once the day-ahead phase begins, the system continuously optimizes and adjusts these plans every 15 minutes. In the real-time phase, the optimization model embeds AC power flow constraints, and solves for the node marginal price reflecting real-time congestion through optimal power flow calculation. Based on this price signal and the system's real-time adjustment needs, the strategy generation unit generates a call sequence for interruptible industrial loads and setpoint adjustment instructions for commercial building air conditioning loads. It also dynamically optimizes the power curves of energy storage stations to achieve the best balance between participating in frequency regulation ancillary services and energy arbitrage. Simultaneously, for weaknesses identified in the safety analysis, it pre-calculates preventative control strategies, including adjusting wind farm output and initiating emergency energy storage discharge, and stores these strategies in the strategy library. S74: The strategy formatting server of the closed-loop control module receives the collaborative control strategy set from the optimization layer and precisely decomposes it into the active power setpoint of a single wind turbine, the charging and discharging power command of the energy storage converter, and the action signal of the flexible load control terminal. The command gateway server encapsulates these specific commands according to the IEC 61850 standard, securely encrypts them through a vertical encryption authentication device, and then sends them to the wind farm centralized monitoring system, the energy storage power station energy management system, and the load aggregator management platform via the message queue middleware. The regional automatic generation control system issues second-level adjustment commands to gas turbines and energy storage units with rapid adjustment capabilities based on regional control errors. The demand-side management platform sends direct load control commands to registered industrial users participating in the response. The user-side intelligent controller automatically executes the agreed load reduction, and the central controller coordinates the power allocation of multiple energy storage power stations to ensure that the state of charge of each energy storage unit is maintained within a healthy range while executing the total system power command. S75: After all control commands are issued, the execution monitoring unit tracks the actual response of each controlled object in real time through the data acquisition and monitoring control system, compares it with the target command, calculates the command execution deviation and response time, and the performance evaluation server comprehensively analyzes the changes in power flow at key transmission sections, the recovery of node voltage, and the stability of system frequency after the control action to evaluate the actual effect of this coordinated control. At the same time, the system continuously compares the actual output of wind and solar power with the ultra-short-term forecast values ​​and calculates the prediction error. These actual operating data and evaluation results are fed back to the digital twin system in real time. The model predictive controller in the adaptive correction unit starts with the latest system state as the starting point and, combined with the rolling updated ultra-short-term forecast, initiates a new cycle of optimization calculation to dynamically correct the subsequent control sequence.

[0031] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A source-network-load-storage collaborative management method, characterized in that: Comprise the following steps: S11: Constructing the global unified real-time database covering sources, networks, loads, and storages; S12: Realizing accurate perception, safety evaluation, and risk early warning of multi-time and space states of the power grid based on digital twinning technology; S13: Generating source, network, load, and storage collaborative operation strategies and control instructions through multi-time scale optimization models; S14: Decomposing and safely issuing system-level instructions to each controllable resource for fast and accurate coordinated control; S15: Forming a "monitoring-evaluation-optimization-correction" closed loop for dynamic adjustment of strategies and continuous optimization of the system. 2.The method according to claim 1, characterized in that: When collecting and standardizing fusion of global multi-source heterogeneous data, the following steps are included: S21: At the outlet of power plants, main power grid hub nodes, important load access points, and energy storage station grid connection points, scale deployment and access of synchronous phasor measurement units capable of collecting voltage, current phasor, and frequency at millisecond level; S22: Through the power plant monitoring system, real-time collection of actual output, active / reactive power upper limit, ramp rate, start / stop state, and predicted wind / solar power data of various generator units to form a panoramic view of power generation capacity; S23: Obtaining real-time topological structure, circuit breaker / switch status, and active power flow, reactive power flow, voltage amplitude, and load rate data of all transmission lines and transformers from the energy management system; S24: Through the advanced measurement system and load control terminal, collecting and summarizing the interruptible power, transferable power, response time, and comfort constraint parameters of industrial large users, commercial buildings, electric vehicle charging stations, and smart home flexible loads; S25: Real-time acquisition of current state of charge, rated power, available charging / discharging capacity, health status, and charging / discharging efficiency curve of each energy storage unit from the battery management system and energy storage converter monitoring system; S26: Applying rule engine and state estimation algorithm to the collected raw data to identify and exclude obviously incorrect bad data, and using historical data interpolation or neighboring node data fitting to repair data missing caused by communication interruption; S27: Unifying data of different collection frequencies and time scales to the core calculation period set by the management and control system through interpolation or aggregation algorithm, and mapping all data to the general CIM / E model to form a standardized object-attribute-measurement database. 3.The source-network-load-storage collaborative management method according to claim 2, characterized in that: When performing multi-time and space state perception and modeling of the system based on digital twinning, the following steps are included: S31: Using wide area measurement system data provided by the synchronous phasor measurement unit, combining the state estimation results, and performing online identification of generator inertia constant and load dynamic characteristic model parameters to update the digital twinning model parameter library; S32: Using weighted least squares method or robust estimation method for state estimation of the whole network with the fused data as measurement input to calculate the node voltage amplitude and phase angle closest to the true operating state, eliminating data noise and errors; S33: Based on the current operating mode, automatically traversing all main component open-circuit fault scenarios one by one, quickly calculating the power flow distribution under each scenario, and screening out weak links causing device overload or voltage overrun. S34: Utilize the linearized system model to analyze the oscillation mode and damping characteristics of the system under small disturbances; simultaneously, evaluate the distance between the current operating point and the voltage stability critical point through continuous power flow or sensitivity analysis; S35: Apply time series analysis or deep learning network algorithms to perform rolling prediction of wind and solar power generation and regional load demand for the next 15 minutes to 4 hours, and generate probabilistic prediction intervals to quantify uncertainty; S36: Based on the individual characteristics and constraints of flexible loads and energy storage, use aggregation theory to aggregate geographically dispersed and diverse resources into a virtual power plant model, depicting its overall power regulation range, response speed, and duration; S37: Integrate safety analysis, stability assessment, and prediction results to quantify the current and near-future comprehensive risk indicators from the perspectives of equipment overload, voltage out-of-limit, frequency stability, and renewable energy fluctuations.

4. The source-network-load-storage collaborative management method according to claim 3, characterized in that: In the process of multi-time scale collaborative optimization decision-making and strategy generation, the following steps are included: S41: Establish a multi-objective optimization function with the lowest total system operating cost, minimum network loss, highest renewable energy consumption rate, and minimum voltage deviation, and consider the physical and operational constraints of all links including sources, networks, loads, and storage; S42: In the day-ahead stage, based on load and new energy prediction, use mixed integer programming or heuristic algorithms to optimize the conventional unit start-stop plan, cross-region trading power, and energy storage charging and discharging plan. In the intraday stage, perform rolling optimization adjustment every 15 minutes; S43: Embed alternating current power flow or direct current power flow constraints in the optimization model, and solve the marginal price of each node reflecting the congestion status and power supply cost through optimal power flow calculation; S44: According to the node marginal price or system regulation demand, design time-of-use price, real-time price, or direct load control signal, and generate calling sequence and power adjustment target value for different types of flexible loads; S45: Considering multiple application scenarios such as price difference arbitrage, fluctuation smoothing, and frequency regulation reserve, dynamically optimize the charging and discharging power curve and operation mode switching logic of energy storage power station based on the trade-off between energy storage life consumption and system benefits; S46: Based on the voltage stability assessment results, cooperatively optimize transformer tap position, shunt capacitor bank switching, distributed power reactive power output, and static var compensator output to form a reactive power resource dispatching strategy for maintaining voltage stability; S47: For potential risks identified by safety analysis, pre-calculate and generate multiple preventive control strategies including adjusting power output, load shedding, and energy storage emergency support, and set trigger thresholds, stored in the strategy library for backup.

5. The method of claim 4, wherein: In the process of hierarchical and zonal control instruction issuance and resource dynamic calling, the following steps are included: S51: Accurately decompose the system-level power and voltage optimization targets into specific power setpoints or state switching instructions for individual generators, energy storage units, and flexible load groups based on aggregation models and resource registration information; S52: Use power-specific wireless private networks or optical fiber networks, and adopt IEC 60870-5-104, IEC 61850, or MQTT protocols to encrypt and issue control instructions to plant monitoring systems, intelligent gateways, or terminal controllers. S53: The regional dispatch center issues second-to-minute adjustment instructions to frequency-regulating units through the automatic generation control system according to the regional control error, and at the same time calls on energy storage and fast load resources to participate in primary frequency regulation; S54: The demand-side management platform sends price signals or direct control instructions to registered flexible load users, and the user-side intelligent energy management system or controller automatically executes load interruption, shifting or reduction operations accordingly; S55: The central controller dynamically allocates total charging and discharging power instructions among multiple energy storage stations according to the real-time state of charge, power capacity and geographical location of each energy storage station, and avoids overcharging or overdischarging of individual units according to the equal incremental rate or optimization algorithm; S56: Active power limit value instructions are issued to wind farm and photovoltaic power station monitoring systems to participate in peak shaving, and reactive power or voltage control instructions are issued to participate in local grid voltage regulation; S57: In the distribution network, active, reactive or voltage reference instructions are issued to power electronic devices for accurate control of power flow, eliminating branch overload and voltage out-of-limit.

6. The source-network-load-storage collaborative management method according to claim 5, characterized in that: When performing closed-loop execution monitoring and adaptive rolling optimization correction, the following steps are included: S61: After the instructions are issued, the actual output, power or state data uploaded by each controlled unit are continuously compared with the target instructions to calculate the instruction execution deviation and response delay time; S62: The trend of the operating indicators of the system key section power flow, node voltage or system frequency is analyzed after the control action is executed, and the actual effect of the strategy on eliminating out-of-limit, stabilizing fluctuations and improving stability is evaluated; S63: The prediction error is calculated by continuously comparing the actual output of wind and light with the ultra-short-term prediction value, and the actual value of load with the prediction value, and the statistical characteristics and spatio-temporal distribution of the error are analyzed; S64: The actual response data is fed back to the digital twin system to drive the model to simulate and replay, analyze the reasons why the control strategy fails to achieve the expected effect, and identify the differences between the model and reality; S65: A new round of collaborative optimization calculation is started in each control period based on the current system state as the initial value and combined with the latest ultra-short-term prediction data, and the optimized control sequence for the next few periods is regenerated; S66: The capacity or efficiency parameters in the equivalent model are calibrated in reverse according to the actual charging and discharging capacity and state of charge change of the energy storage unit; S67: The preventive control strategies stored in the strategy library are periodically or triggeredly checked and updated according to the actual control effect and changes in system operation mode.

7. The method of claim 1-6, wherein the method further comprises: A source-grid-load-storage collaborative management system, comprising the following modules: Panoramic perception and digital twin module: used for collecting, fusing and cleaning real-time running data of the whole domain, and constructing a digital twin model that evolves synchronously with the physical power grid; Collaborative optimization decision-making module: used for generating a set of collaborative control strategies covering the four aspects of source, grid, load and storage by solving a multi-objective, multi-constrained optimization model based on the system state and prediction information provided by the panoramic perception module; Closed-loop control and execution management module: responsible for decomposing and issuing the strategy instructions generated by the optimization decision-making module to each execution terminal, and monitoring the instruction execution, and making adaptive adjustments according to the feedback. 8.The source-network-load-storage collaborative management system according to claim 7, characterized in that: Panoramic perception and digital twin module, comprising: A11: data acquisition and fusion unit, comprising data acquisition gateway, time series database and data fusion server, for collecting multi-source heterogeneous original operation data from source, network, load and storage terminal devices, and processing to form a system-level unified time series database; A12: model management and simulation calculation unit, comprising model server, high-performance computing cluster and power system analysis software package, for establishing and maintaining the digital twin model of the power grid, and receiving the fused real-time data to drive the model operation; A13: state assessment and prediction and early warning unit, comprising risk assessment server, prediction engine server and visual alarm workstation, for assessing the safety and stability of the system as a whole and the operation risk based on the simulation calculation results and real-time data of the digital twin model, and performing ultra-short-term prediction on key variables. 9.The source-network-load-storage collaborative management system according to claim 8, characterized in that: Collaborative optimization decision module, comprising: A21: optimization calculation engine unit, comprising mathematical programming solver, heuristic algorithm calculation server and optimization task scheduler, for solving large-scale, nonlinear source-network-load-storage collaborative optimization problems and calculating the system operation mode and resource allocation scheme with the lowest total cost and the highest safety; A22: strategy generation and management unit, comprising strategy formatting server, strategy database and strategy checking software, for converting the mathematical results calculated by the optimization engine into specific and executable control strategy instructions; A23: market and price decision unit, comprising LMP calculation server, demand response management platform and market clearing engine, for specifically responsible for interfacing with the electricity market, calculating node marginal price, designing demand side response scheme, and generating corresponding market clearing and settlement signals.

10. The source-network-load-storage collaborative management system of claim 9, wherein: Closed-loop control and execution management module, comprising: A31: instruction distribution and communication unit, comprising instruction gateway server, security encryption device and message queue middleware, for receiving upper-level strategies, analyzing and decomposing them into specific action instructions for individual controllable devices; A32: execution monitoring and verification unit, comprising data acquisition and monitoring control system, performance evaluation server and alarm management server, for receiving real-time feedback information uploaded by each execution terminal, comparing the control instructions with the actual actions of the devices, and calculating the instruction execution rate and response delay; A33: adaptive correction and rolling optimization unit, comprising model predictive controller, feedback data interface server and rolling optimization trigger, for dynamically triggering a new round of optimization calculation based on the feedback of the execution monitoring unit and the latest system state perception data, and adjusting and correcting the subsequent control strategy in real time.

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