Source-grid-load-storage integrated real-time simulation and optimization control platform based on digital twinning

Abstract

This invention relates to a real-time simulation and optimization control platform for integrated power generation, grid, load, and storage systems based on digital twins, belonging to the field of power system automation technology. The method includes: a twin model construction and self-calibration unit integrating multi-physics parameters, equipment, and grid characteristics to construct a model; a dual-algorithm collaborative optimization control unit constructing a bidirectional optimization framework through model predictive control and multi-agent reinforcement learning, coupled with a multi-dimensional adaptive reward mechanism to output control commands; a multi-source data fusion and planning linkage unit completing semantic annotation and association mapping of multiple types of data, adjusting production plans and decomposing loads based on simulation results; and a green electricity trading and scheduling coordination unit incorporating core elements of green electricity trading into the optimization objective to generate a trading and scheduling coordination scheme. This method achieves accurate fusion of multi-source data from the power generation, grid, load, and storage system, effectively solving the defects of unscientific planning adjustments and insufficient coordination in existing technologies, and improving the safety and economy of power grid operation.

Description

Technical Field

[0001] This invention belongs to the field of power system automation technology, specifically relating to a real-time simulation and optimization control platform for integrated power generation, grid, load and storage based on digital twins. Background Technology

[0002] As the global energy transition accelerates, new energy power generation technologies, represented by wind and solar power, are being connected to the grid on a large scale. The traditional power system is gradually evolving into a new type of power system characterized by coordinated interaction between power generation, grid, load, and storage. Against this backdrop, the power system's power supply structure and load characteristics are undergoing profound changes: new energy power generation is characterized by volatility, randomness, and intermittency, significantly increasing the difficulty of grid dispatching; simultaneously, the flexible adjustment potential of industrial, commercial, and residential loads is gradually being released, and the large-scale application of energy storage devices further enriches system regulation methods. This requires the power system to possess more accurate operational status perception, more efficient data processing capabilities, and a more scientific level of dispatching plan development.

[0003] The core objective of coordinated dispatching of energy sources, grid, load, and energy storage is to optimize the allocation of energy resources, ensure the safe and stable operation of the power grid, and improve energy utilization efficiency. Effective integration of multi-source data and dynamic optimization of dispatching plans are key aspects, involving the integration and analysis of various data types, including environmental equipment operation data, production planning data, and real-time measurement data, as well as a series of operations based on data, such as production plan adjustments, load decomposition, and constraints on photovoltaic output and energy storage charging and discharging.

[0004] Currently, in the technical practices related to the coordinated scheduling of power generation, grid, load, and storage, data fusion, model simulation, and other technical means have been gradually introduced, but there are still many technical shortcomings that urgently need to be addressed: First, the fusion of multi-source data is ineffective. Environmental equipment operation data, production planning business data, and other data originate from different systems, resulting in issues such as inconsistent data formats, inconsistent semantic descriptions, and scattered data storage. Existing technologies often use simple data splicing or format conversion methods to process multi-source data, lacking standardized semantic annotation and precise association mapping mechanisms. This leads to poor data correlation and inconsistent data quality, failing to provide reliable data support for subsequent plan adjustments and load decomposition.

[0005] Secondly, the adjustment of production plans and load allocation lack scientific rigor and rationality. Current technologies often rely solely on historical operating experience or single-dimensional operating data when adjusting production plans and allocating loads, failing to fully integrate real-time measurement data and simulation results from digital twin models. They also fail to comprehensively consider constraints such as the reasonable range of photovoltaic output, the power and time-limited charging and discharging of energy storage. This leads to a mismatch between the allocated load tasks and the operating characteristics and carrying capacity of each source-grid-load-storage entity, easily resulting in overload or resource idleness, thus affecting system operating efficiency.

[0006] Third, the application of digital twin technology is not in-depth, resulting in a disconnect between simulation and actual operation. Although some technical solutions incorporate digital twin models, the mapping accuracy of physical entities during model construction is insufficient, and the multi-physics parameters and core operating characteristics of the equipment are not fully integrated, leading to significant deviations between simulation results and the actual operating state of the system. Furthermore, existing models are mostly used for static simulation analysis, unable to respond in real time to dynamic changes in the system's operating state, and therefore difficult to accurately guide the dynamic adjustment of production plans and load decomposition.

[0007] Fourth, the dynamic adaptability of the scheduling plan is insufficient. Existing production load breakdown plans are mostly static plans, lacking a dynamic tracking and feedback adjustment mechanism for real-time operational data. When new energy power generation fluctuates, load demand changes, or equipment operating conditions are abnormal, the plan cannot be adjusted in a timely manner, easily leading to a disconnect between the plan and actual operational needs. This affects the coordination and cooperation among power generation, grid, load, and storage entities, and may even threaten the safe and stable operation of the power grid.

[0008] In summary, under the scenario of coordinated dispatch of power generation, grid, load and storage in the new power system, existing technologies have significant shortcomings in multi-source data fusion, plan optimization and adjustment, scientific load decomposition and application of digital twin models, making it difficult to meet the needs of efficient, safe and economical system operation. Summary of the Invention

[0009] To address the aforementioned problems in the existing technology, this invention provides a real-time simulation and optimization control platform for integrated source-grid-load-storage systems based on digital twins. The objective of this invention can be achieved through the following technical solutions: include: The twin model construction and self-calibration unit acquires multi-physics field parameters, integrates the operating characteristics of source-grid-load-storage equipment with the macroscopic system characteristics of the power grid, constructs a digital twin model and performs simulation; based on real-time measurement data and production plan deviations, it corrects the parameters of the digital twin model; it presets system disturbance scenarios, combines real-time data to predict operational risks, and generates alternative risk control strategies; A dual-algorithm collaborative optimization control unit is constructed, which establishes a two-way framework of global planning and distributed decision-making. Model predictive control performs global short-term deterministic optimization, while multi-agent reinforcement learning regulates distributed agent optimization. A multi-dimensional indicator adaptive reward mechanism is designed to quantify prediction deviations and incorporate them into algorithm constraints, outputting source-grid-load-storage collaborative optimization control commands. The multi-source data fusion and planning linkage unit performs semantic annotation and correlation mapping on environmental equipment operation data and production planning business data; based on real-time measurement data and digital twin model simulation results, it adjusts the production plan and decomposes the load, and inversely constrains the photovoltaic output and energy storage charging and discharging plans; it integrates the adjusted production plan, trains and optimizes the digital twin model, and forms a production load decomposition plan; The green electricity trading and dispatch coordination unit incorporates the core elements of the green electricity trading market into the source-grid-load-storage optimization objectives. It simulates the dispatch effects of different trading strategies through the digital twin model; it aggregates flexible loads to participate in green electricity trading and adjusts electricity consumption periods; it quantifies carbon emission reductions based on operational data, summarizes comprehensive optimization objectives, and generates a green electricity trading and energy dispatch coordination scheme.

[0010] Specifically, the process of constructing the digital twin model and performing simulation is as follows: It includes: physical entity mapping layer, data fusion layer, model layer, and simulation inference layer; Input multiphysics parameters, equipment operating characteristics, real-time measurement data, and production plan data; The physical entity mapping layer maps the physical structure of the source-grid-load-storage equipment and the power grid; the data fusion layer integrates multiple physical field parameters and equipment operating characteristics; the model building layer builds a mechanism model based on the fused data; and the simulation and deduction layer imports real-time measurement and production plan data and outputs simulation information of operating status and deduction conclusions of dynamic change trends.

[0011] Specifically, the process of setting up the system disturbance scenarios is as follows: setting up abnormal scenarios for the entire operation of the source-grid-load-storage system; combining historical system fault records and operating experience, distinguishing three types of disturbances: equipment operation faults, load mutations, and sudden changes in environmental parameters; setting trigger conditions and impact ranges for each type of disturbance, and establishing a disturbance scenario library covering all scenarios.

[0012] Specifically, the process of constructing the two-way framework of global coordination and distributed decision-making is as follows: Based on the operational requirements of the power generation, grid, load, and storage system and the power grid operation specifications, the overall system optimization objectives and constraint boundaries are preset; The system is divided into independent sub-units based on the main entities of source, grid, load and storage. Sub-objectives matching the overall goals are assigned to each sub-unit to guide each sub-unit to make distributed decisions and local optimizations based on its own operating characteristics. Establish standardized data interaction protocols, build two-way data interaction channels, break down the overall optimization goal into executable sub-tasks of each sub-unit, and collect the local optimization results of each sub-unit simultaneously and summarize them at the global level.

[0013] Specifically, the process of the model predictive control performing global short-term deterministic optimization is as follows: based on historical operating data and digital twin model simulation results, predict the system load demand and photovoltaic output in the short term; with system power balance and optimal operating efficiency as the core objectives, solve for the global-level scheduling scheme.

[0014] Specifically, the process of multi-agent reinforcement learning to regulate the optimization of distributed entities is as follows: intelligent agents are constructed for each distributed entity according to source, network, load, and storage, and local optimization goals and interaction rules are set for each intelligent agent; using historical operation data and real-time status data as learning samples, each intelligent agent outputs the optimal regulation strategy under different scenarios through interactive learning; and the distributed entities are dynamically optimized and regulated through collaborative interaction among intelligent agents.

[0015] Specifically, the process of designing the multi-dimensional indicator adaptive reward mechanism is as follows: The system includes four key indicators: photovoltaic grid integration rate, operational economy, grid stability, and carbon emission reduction benefits, forming a multi-dimensional evaluation system. Analyze the importance of various indicators under different operating scenarios and establish dynamic adjustment rules for indicator weights; Based on the control effects and indicator achievement of each agent, the reward value calculated quantitatively is used to provide positive feedback on the agent control behavior that conforms to the global optimization direction, and to correct the agent control strategy that deviates from the global optimization direction.

[0016] Specifically, the process of semantic annotation and association mapping of environmental equipment operation data and production planning business data is as follows: the environmental equipment operation data is annotated with three types of semantic information: data source, collection time, and data type; the production planning business data is annotated with three types of semantic information: planning cycle, load type, and production node; and a mapping rule between the two types of data is established based on business logic and data association relationship.

[0017] Specifically, the process of the reverse constraint photovoltaic output and energy storage charging and discharging plan is as follows: based on the adjusted production plan and load decomposition results, analyze the current carrying capacity and operating constraints of the power grid; combine the system operating limits obtained by digital twin model simulation, preset a reasonable range of photovoltaic output and upper limit of energy storage charging and discharging power, and time period restrictions; embed the constraints as core constraints into the compilation stage of photovoltaic output and energy storage charging and discharging plan.

[0018] Specifically, the process of forming the production load breakdown plan is as follows: Based on the preprocessed real-time measurement data and the simulation results of the digital twin model, the initial production plan was adjusted; The adjusted production plan is broken down into load tasks corresponding to each entity by dividing the load decomposition dimensions. Simultaneously retrieve the reasonable range of photovoltaic output and the energy storage charging and discharging limits determined by the reverse constraints to verify the feasibility of each load task; Prioritize and allocate resources to the load tasks that pass the verification, and summarize them to form a formal production load decomposition plan.

[0019] Specifically, the process of simulating the scheduling effect of different trading strategies through the digital twin model is as follows: preset green electricity trading market trading strategies, and clarify the three core parameters of each strategy: trading period, bidding method, and trading volume; input the different trading strategy parameters and the current system operation data into the digital twin model to simulate the energy flow path, supply and demand balance and economic benefits under each strategy; and output the quantitative evaluation results of the scheduling effect of each strategy.

[0020] Specifically, the process of generating a coordinated green electricity trading and energy dispatch scheme is as follows: summarizing the simulation evaluation results of different trading strategies, combining the feedback information of system regulation potential, and selecting trading strategies that are both economical and feasible; combining the selected trading strategies with the source-grid-load-storage dispatch requirements, integrating three categories of content: photovoltaic consumption plan, energy storage charging and discharging strategy, and flexible load adjustment scheme; and incorporating the carbon emission reduction quantification results for comprehensive optimization.

[0021] The beneficial effects of this invention are as follows: (1) By setting up a twin model construction and self-calibration unit and a dual-algorithm collaborative optimization control unit, the simulation prediction capability and collaborative optimization control level are improved. Among them, the twin model construction and self-calibration unit integrates multi-physics parameters, equipment and power grid characteristics to build a multi-level model, and completes self-calibration with real-time data, which greatly reduces the deviation between simulation and actual operation. At the same time, it presets a full-scenario disturbance scenario library to predict operation risks and generate alternative control strategies to ensure the safe and stable operation of the power grid. The dual-algorithm collaborative optimization control unit builds a two-way framework of global planning and distributed decision-making. It achieves global short-term optimization through model predictive control and distributed precise control through multi-agent reinforcement learning. With the help of a multi-dimensional adaptive reward mechanism, it guides the control direction to be consistent with the global goal, solves the problem of imbalance between global and local optimization and insufficient control targeting in the existing technology, and improves the collaborative effect of various subjects. (2) By setting up a multi-source data fusion and planning linkage unit and a green electricity trading and scheduling coordination unit, the value of multi-source data was efficiently mined, and the scientific formulation of plans and coordinated scheduling of green electricity were realized. The multi-source data fusion and planning linkage unit solved the problems of inconsistent formats and poor correlation of multi-source data through standardized semantic annotation and accurate correlation mapping, providing reliable support for plan adjustment. Based on real-time data and twin simulation results, the production plan was adjusted, and the feasibility of load tasks was verified by combining photovoltaic output and energy storage charging and discharging constraints, so as to realize the scientific decomposition of load and reasonable resource allocation and improve energy allocation efficiency. The green electricity trading and scheduling coordination unit incorporates the core elements of green electricity trading into the optimization objectives, simulates the effect of trading strategies through twin models and selects the optimal solution, aggregates flexible loads to participate in trading and incorporates carbon emission reduction quantification results to optimize the coordination solution, solves the problem of disconnect between green electricity trading and energy scheduling, helps green electricity to be efficiently consumed and develop in a low-carbon manner, and achieves a win-win situation for economic and ecological benefits. Attached Figure Description

[0022] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0023] Figure 1 This is a system architecture diagram of a real-time simulation and optimization control platform for integrated source-grid-load-storage based on digital twins, according to the present invention. Figure 2 This is a data flow diagram of a real-time simulation and optimization control platform for integrated source-grid-load-storage based on digital twins, according to the present invention. Detailed Implementation

[0024] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0025] Please see Figure 1-2 A real-time simulation and optimization control platform for integrated source-grid-load-storage based on digital twins; include: The twin model construction and self-calibration unit acquires multi-physics field parameters, integrates the operating characteristics of source-grid-load-storage equipment with the macroscopic system characteristics of the power grid, constructs a digital twin model and performs simulation; based on real-time measurement data and production plan deviations, it corrects the parameters of the digital twin model; it presets system disturbance scenarios, combines real-time data to predict operational risks, and generates alternative risk control strategies; A dual-algorithm collaborative optimization control unit is constructed, which establishes a two-way framework of global planning and distributed decision-making. Model predictive control performs global short-term deterministic optimization, while multi-agent reinforcement learning regulates distributed agent optimization. A multi-dimensional indicator adaptive reward mechanism is designed to quantify prediction deviations and incorporate them into algorithm constraints, outputting source-grid-load-storage collaborative optimization control commands. The multi-source data fusion and planning linkage unit performs semantic annotation and correlation mapping on environmental equipment operation data and production planning business data; based on real-time measurement data and digital twin model simulation results, it adjusts the production plan and decomposes the load, and inversely constrains the photovoltaic output and energy storage charging and discharging plans; it integrates the adjusted production plan, trains and optimizes the digital twin model, and forms a production load decomposition plan; The green electricity trading and dispatch coordination unit incorporates the core elements of the green electricity trading market into the source-grid-load-storage optimization objectives. It simulates the dispatch effects of different trading strategies through the digital twin model; it aggregates flexible loads to participate in green electricity trading and adjusts electricity consumption periods; it quantifies carbon emission reductions based on operational data, summarizes comprehensive optimization objectives, and generates a green electricity trading and energy dispatch coordination scheme.

[0026] In this embodiment, the multi-physics parameters specifically include electrical field parameters (voltage, current, power, frequency, impedance), meteorological field parameters (light intensity, wind speed, temperature, humidity, precipitation), and thermodynamic field parameters (equipment operating temperature, heat dissipation efficiency, ambient temperature difference), which are the basic data for constructing digital twin models and realizing accurate mapping of physical entities.

[0027] In this embodiment, the operating characteristics of the source-grid-load-storage equipment refer to the inherent operating rules and technical parameters of the equipment in each link of the source-grid-load-storage system, which are defined into four categories of equipment: the output characteristics, start-stop response speed, and power regulation range of the "source" side equipment (photovoltaic inverters, wind turbines, generator sets); the impedance characteristics, transmission capacity, and voltage loss characteristics of the "grid" side equipment (transformers, transmission and distribution lines, switchgear); the electricity consumption period patterns, load fluctuation coefficient, and flexible regulation potential of the "load" side equipment (industrial loads, commercial loads, residential loads); and the charging and discharging efficiency, capacity decay characteristics, and upper limit of charging and discharging power of the "storage" side equipment (lithium batteries, energy storage power stations).

[0028] In this embodiment, the macroscopic system characteristics of the power grid refer to the characteristics that reflect the system's operating rules and technical boundaries from the overall power grid level. Specifically, these include the power grid topology, transmission channel capacity, voltage level hierarchy, system inertia level, dispatch jurisdiction, power grid security and stability constraint standards, and source-load spatiotemporal distribution characteristics. These are the core basis for formulating global optimization objectives and constraint boundaries.

[0029] In this embodiment, algorithm constraints refer to the restrictions embedded in the two core algorithms of model predictive control and multi-agent reinforcement learning. Specifically, they cover three types of constraints: equipment safety constraints (such as the upper limit of energy storage charging and discharging power and the threshold of transformer load rate), system operation constraints (such as power supply and demand balance, frequency stability range, and voltage qualification range), and target-oriented constraints (such as the lower limit of photovoltaic absorption rate, the benchmark value of carbon emission reduction benefits, and the threshold of operation economic indicators). At the same time, dynamic constraints that incorporate the quantified value of prediction deviation are also included.

[0030] In this embodiment, environmental equipment operation data and production planning business data are two collective terms for two types of differentiated data, specifically defined as follows: Environmental equipment operation data refers to real-time data collected by sensors and monitoring terminals, including environmental status data (ambient temperature, humidity, light intensity) and equipment operation status data (equipment start / stop status, operating power, fault alarm information, maintenance records), which has the characteristics of real-time and objectivity. Production planning business data refers to manually compiled planned and management data, including power generation plans (photovoltaic and wind power output plans), power consumption plans (industrial and residential load plans), maintenance plans (equipment shutdown and maintenance periods), and dispatch plans (power allocation schemes), which are characterized by their forward-looking and guiding nature.

[0031] In this embodiment, the core elements of the green electricity trading market refer to the key components of green electricity trading that are incorporated into the source-grid-load-storage optimization target. Specifically, these include trading entities (power generation companies, electricity sales companies, and electricity users), core trading parameters (trading time period, bidding method, trading volume, and transaction price), trading rules (performance period and deviation assessment standards), and added value elements (green electricity certificate issuance standards and carbon emission reduction measurement methods).

[0032] Specifically, the process of constructing the digital twin model and performing simulation is as follows: It includes: physical entity mapping layer, data fusion layer, model layer, and simulation inference layer; Input multiphysics parameters, equipment operating characteristics, real-time measurement data, and production plan data; The physical entity mapping layer maps the physical structure of the source-grid-load-storage equipment and the power grid; the data fusion layer integrates multiple physical field parameters and equipment operating characteristics; the model building layer builds a mechanism model based on the fused data; and the simulation and deduction layer imports real-time measurement and production plan data and outputs simulation information of operating status and deduction conclusions of dynamic change trends.

[0033] Specifically, the process of setting up the system disturbance scenarios is as follows: setting up abnormal scenarios for the entire operation of the source-grid-load-storage system; combining historical system fault records and operating experience, distinguishing three types of disturbances: equipment operation faults, load mutations, and sudden changes in environmental parameters; setting trigger conditions and impact ranges for each type of disturbance, and establishing a disturbance scenario library covering all scenarios.

[0034] Specifically, the process of constructing the two-way framework of global coordination and distributed decision-making is as follows: Based on the operational requirements of the power generation, grid, load, and storage system and the power grid operation specifications, the overall system optimization objectives and constraint boundaries are preset; The system is divided into independent sub-units based on the main entities of source, grid, load and storage. Sub-objectives matching the overall goals are assigned to each sub-unit to guide each sub-unit to make distributed decisions and local optimizations based on its own operating characteristics. Establish standardized data interaction protocols, build two-way data interaction channels, break down the overall optimization goal into executable sub-tasks of each sub-unit, and collect the local optimization results of each sub-unit simultaneously and summarize them at the global level.

[0035] Specifically, the process of the model predictive control performing global short-term deterministic optimization is as follows: based on historical operating data and digital twin model simulation results, predict the system load demand and photovoltaic output in the short term; with system power balance and optimal operating efficiency as the core objectives, solve for the global-level scheduling scheme.

[0036] Specifically, the process of multi-agent reinforcement learning to regulate the optimization of distributed entities is as follows: intelligent agents are constructed for each distributed entity according to source, network, load, and storage, and local optimization goals and interaction rules are set for each intelligent agent; using historical operation data and real-time status data as learning samples, each intelligent agent outputs the optimal regulation strategy under different scenarios through interactive learning; and the distributed entities are dynamically optimized and regulated through collaborative interaction among intelligent agents.

[0037] Specifically, the process of designing the multi-dimensional indicator adaptive reward mechanism is as follows: The system includes four key indicators: photovoltaic grid integration rate, operational economy, grid stability, and carbon emission reduction benefits, forming a multi-dimensional evaluation system. Analyze the importance of various indicators under different operating scenarios and establish dynamic adjustment rules for indicator weights; Based on the control effects and indicator achievement of each agent, the reward value calculated quantitatively is used to provide positive feedback on the agent control behavior that conforms to the global optimization direction, and to correct the agent control strategy that deviates from the global optimization direction.

[0038] Specifically, the process of semantic annotation and association mapping of environmental equipment operation data and production planning business data is as follows: the environmental equipment operation data is annotated with three types of semantic information: data source, collection time, and data type; the production planning business data is annotated with three types of semantic information: planning cycle, load type, and production node; and a mapping rule between the two types of data is established based on business logic and data association relationship.

[0039] Specifically, the process of the reverse constraint photovoltaic output and energy storage charging and discharging plan is as follows: based on the adjusted production plan and load decomposition results, analyze the current carrying capacity and operating constraints of the power grid; combine the system operating limits obtained by digital twin model simulation, preset a reasonable range of photovoltaic output and upper limit of energy storage charging and discharging power, and time period restrictions; embed the constraints as core constraints into the compilation stage of photovoltaic output and energy storage charging and discharging plan.

[0040] Specifically, the process of forming the production load breakdown plan is as follows: Based on the preprocessed real-time measurement data and the simulation results of the digital twin model, the initial production plan was adjusted; The adjusted production plan is broken down into load tasks corresponding to each entity by dividing the load decomposition dimensions. Simultaneously retrieve the reasonable range of photovoltaic output and the energy storage charging and discharging limits determined by the reverse constraints to verify the feasibility of each load task; Prioritize and allocate resources to the load tasks that pass the verification, and summarize them to form a formal production load decomposition plan.

[0041] Specifically, the process of simulating the scheduling effect of different trading strategies through the digital twin model is as follows: preset green electricity trading market trading strategies, and clarify the three core parameters of each strategy: trading period, bidding method, and trading volume; input the different trading strategy parameters and the current system operation data into the digital twin model to simulate the energy flow path, supply and demand balance and economic benefits under each strategy; and output the quantitative evaluation results of the scheduling effect of each strategy.

[0042] Specifically, the process of generating a coordinated green electricity trading and energy dispatch scheme is as follows: summarizing the simulation evaluation results of different trading strategies, combining the feedback information of system regulation potential, and selecting trading strategies that are both economical and feasible; combining the selected trading strategies with the source-grid-load-storage dispatch requirements, integrating three categories of content: photovoltaic consumption plan, energy storage charging and discharging strategy, and flexible load adjustment scheme; and incorporating the carbon emission reduction quantification results for comprehensive optimization.

[0043] In this embodiment, MPC (Model Predictive Control) and MARL (Multi-Agent Reinforcement Learning) work together to achieve hierarchical optimization of both global and local aspects. The specific working process and technical means are as follows: For MPC, a closed-loop working mode of prediction-optimization-rolling execution is adopted: First, based on the historical operating data of the past 3 months (approximately 100,000 samples) and the simulation results output by the digital twin model, the system load demand and photovoltaic output in the next hour are predicted using the ARIMA time series prediction algorithm, with a prediction step size of 5 minutes, generating data for 12 prediction time nodes; then, with the core objectives of system power balance and optimal operating efficiency, a quadratic programming optimization model is constructed, with constraints including the upper limit of energy storage charging and discharging power (≤500kW), transformer load rate threshold (≤85%), and voltage fluctuation range (±5% of rated voltage). The Gurobi optimization solver is used to complete the solution within 0.5 seconds, deriving the scheduling scheme for each time period at the global level; finally, a rolling optimization strategy is adopted, updating the prediction information and resolving every 5 minutes based on the latest real-time measurement data (sampling frequency 1Hz); For MARL, the implementation path adopts multi-agent interaction-offline training-online deployment: 11 independent agents are divided according to the main entities of source, grid, load and storage (2 on the photovoltaic side, 3 on the grid side, 4 on the load side and 2 on the energy storage side). Each agent adopts the DQN (Deep Q Network) algorithm architecture, with the input layer dimension set to 20 dimensions (covering its own operating status, real-time power, and interaction data of surrounding entities, etc.), the hidden layer has 2 layers (128 neurons in each layer), and the output layer has 6 control actions (such as energy storage charging / discharging power adjustment, flexible load start-stop, etc.). Using historical operational data (approximately 200,000 samples) from the past 6 months and real-time status data as learning samples, offline training was conducted under the TensorFlow framework. The number of training iterations was set to 50,000, the learning rate was 0.001, and the convergence threshold of the objective function was set to 0.0001. During the online operation phase, each agent collected its own and surrounding entities' status data every 2 seconds. Collaborative decision-making was achieved through preset interaction rules (such as the power complementarity rules between the load-side agent and the energy storage-side agent), and the control results were fed back to the global decision-making layer in real time.

[0044] In this embodiment, the specific implementation process of the digital twin model is as follows: The construction phase is divided into four layers. The physical entity mapping layer uses laser scanning combined with building information modeling technology to perform 3D modeling of 1000+ core devices and power transmission and distribution lines; the data fusion layer preprocesses multi-physics parameters by edge nodes, and completes semantic integration after denoising using the Kalman filter algorithm (filtering accuracy ≥98.5%); the model layer builds and encapsulates mechanism models such as power flow calculation and photovoltaic power output prediction; the simulation layer integrates a 3D visualization engine to construct a visualized scene. The simulation simulation adopts a real-time data-driven mode, receiving real-time measurement and production plan data every 500ms, and simulating the system operation status in 1-minute increments; every hour, based on the deviation between real-time data and simulation results (threshold 3%), the model parameters are corrected using the least squares method to ensure that the simulation deviation is controlled within 2%.

[0045] In this embodiment, the specific implementation process of the multi-dimensional indicator adaptive reward mechanism is as follows: When constructing the mechanism, four core indicators, such as photovoltaic absorption rate and operational economy, are selected. The analytic hierarchy process is used to determine the initial weights and establish dynamic adjustment rules (such as increasing the weight of photovoltaic absorption rate in the scenario of large-scale new energy generation). The weights are automatically updated every hour according to the operational scenario. During the application phase of the mechanism, intelligent agent control data is collected every 10 minutes, and the reward value (range [-10, 10]) is quantified through a linear formula. The reward value is fed back to the deep Q-network algorithm of the intelligent agent. The selection probability of optimization actions with reward values ​​≥ 5 is strengthened, and the probability of deviation actions with reward values ​​≤ -3 is reduced and strategy iteration is triggered to ensure that the control direction is consistent with the global goal.

[0046] In this embodiment, the deployment of the digital twin-based integrated real-time simulation and optimization control platform for power generation, grid, load and storage of this invention in an industrial park is taken as an example. It is used to coordinate the collaborative operation of photovoltaic power plants, energy storage power plants, industrial flexible loads and distribution networks. The specific implementation process is as follows: In the early morning, the platform's multi-source data fusion and planning linkage unit is activated, collecting environmental and equipment operation data such as light intensity and equipment operating power within the park, while simultaneously accessing production planning business data such as the daily production line electricity consumption plan and photovoltaic output forecast plan. Subsequently, semantic annotation and association mapping are performed on the two types of data to form a standardized dataset, providing data support for subsequent modeling and simulation.

[0047] The twin model construction and self-calibration unit calls upon the aforementioned dataset and, combining system multi-physics parameters, the operating characteristics of source-grid-load-storage equipment, and the macroscopic system characteristics of the power grid, constructs a hierarchical digital twin model. After inputting data, the model simulates and extrapolates the system's power flow, load response, and other operating states over the next b hours, outputting results such as photovoltaic output and load demand changes. Simultaneously, based on historical fault data from the past a months, it pre-sets a type a disturbance scenario database to predict potential risks such as sudden changes in solar irradiance and sudden load surges on the current day.

[0048] Production lines started up gradually in the morning, and the load increased rapidly. The platform's dual-algorithm collaborative optimization control unit intervened: Based on historical data and simulation results, the model predictive control module predicts the load and photovoltaic output changes in the next b hours, builds an optimization model in c-minute steps, incorporates constraints such as the upper limit of energy storage charging and discharging power ≤ d kW and transformer load rate ≤ e%, and generates a global scheduling scheme. The multi-agent reinforcement learning module divides the system into f agents, corresponding to photovoltaic, energy storage, various production lines, and the main distribution network, respectively, to collect real-time operational status data and make collaborative decisions. A multi-dimensional indicator adaptive reward mechanism is activated simultaneously, dynamically adjusting indicator weights based on scenarios of increased load. It provides positive incentives for actions that align with the global objective, such as staggered startup of flexible production lines, and provides negative corrections for deviations, such as excessive discharge of energy storage.

[0049] At 10:00 AM, the twin model, combined with real-time data, completed self-calibration and discovered that the day's solar irradiance was higher than estimated, indicating that photovoltaic (PV) output would exceed expectations. The platform then adjusted its production plan, allocating some production line loads to peak PV output periods. It also verified the feasibility of a reasonable PV output range and energy storage charging / discharging limitations, ultimately forming a final production load allocation plan. Simultaneously, the green electricity trading and scheduling coordination unit incorporated the PV excess output information into its optimization objectives. Through twin model simulations of different trading strategies, it aggregated flexible load adjustments for different electricity consumption periods, quantified the day's carbon emission reductions, and finally generated a green electricity trading and energy dispatch coordination solution, selling the excess green electricity to businesses in the surrounding industrial park.

[0050] In the afternoon, the platform detected a disturbance signal of a sudden drop in light intensity, immediately retrieved the disturbance scenario database, predicted the potential power imbalance risk caused by the sudden drop in photovoltaic output, and quickly generated a Class A alternative control strategy, such as emergency discharge of energy storage to replenish energy or temporary load reduction of flexible loads, and pushed it to the dispatch personnel for backup.

[0051] By the end of the day's scheduling, the park's green electricity consumption rate had increased by g percentage points, carbon emission reduction reached h tons, and system operation deviation was controlled within i%, fully verifying the practical value of the invention.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A real-time simulation and optimization control platform integrating source, grid, load, and storage based on digital twins, characterized in that, include: The twin model construction and self-calibration unit acquires multi-physics field parameters, integrates the operating characteristics of source-grid-load-storage equipment with the macroscopic system characteristics of the power grid, constructs a digital twin model, and performs simulation deduction. Based on the deviation between real-time measurement data and production plan, the parameters of the digital twin model are corrected; Pre-determine system disturbance scenarios, combine real-time data to predict operational risks, and generate alternative risk control strategies; A dual-algorithm collaborative optimization control unit is constructed, which establishes a two-way framework of global planning and distributed decision-making. Model predictive control performs global short-term deterministic optimization, while multi-agent reinforcement learning regulates distributed agent optimization. A multi-dimensional indicator adaptive reward mechanism is designed to quantify prediction deviations and incorporate them into algorithm constraints, outputting source-grid-load-storage collaborative optimization control commands. The multi-source data fusion and planning linkage unit performs semantic annotation and association mapping on environmental equipment operation data and production planning business data; Based on real-time measurement data and digital twin model simulation results, the production plan is adjusted and the load is decomposed to inversely constrain the photovoltaic output and energy storage charging and discharging plan; The adjusted production plan is integrated, and the digital twin model is trained and optimized to form a production load decomposition plan. The green electricity trading and dispatch coordination unit incorporates the core elements of the green electricity trading market into the source-grid-load-storage optimization objectives. It simulates the dispatch effects of different trading strategies through the digital twin model; it aggregates flexible loads to participate in green electricity trading and adjusts electricity consumption periods; it quantifies carbon emission reductions based on operational data, summarizes comprehensive optimization objectives, and generates a green electricity trading and energy dispatch coordination scheme.

2. The system according to claim 1, characterized in that, The specific process of constructing a digital twin model and performing simulation is as follows: It includes: physical entity mapping layer, data fusion layer, model layer, and simulation inference layer; Input multiphysics parameters, equipment operating characteristics, real-time measurement data, and production plan data; The physical entity mapping layer maps the physical structure of the source-grid-load-storage equipment and the power grid; the data fusion layer integrates multiple physical field parameters and equipment operating characteristics; the model building layer builds a mechanism model based on the fused data; and the simulation and deduction layer imports real-time measurement and production plan data and outputs simulation information of operating status and deduction conclusions of dynamic change trends.

3. The system according to claim 1, characterized in that, The specific process of the preset system disturbance scenario is as follows: preset abnormal scenarios for the entire operation of the source-grid-load-storage system; combine historical system fault records and operating experience to distinguish three types of disturbances: equipment operation faults, load mutations, and sudden changes in environmental parameters; set trigger conditions and impact ranges for each type of disturbance, and establish a disturbance scenario library covering the entire scenario.

4. The system according to claim 1, characterized in that, The specific process of constructing the two-way framework of global coordination and distributed decision-making is as follows: Based on the operational requirements of the power generation, grid, load, and storage system and the power grid operation specifications, the overall system optimization objectives and constraint boundaries are preset; The system is divided into independent sub-units based on the main entities of source, grid, load and storage. Sub-objectives matching the overall goals are assigned to each sub-unit to guide each sub-unit to make distributed decisions and local optimizations based on its own operating characteristics. Establish standardized data interaction protocols, build two-way data interaction channels, break down the overall optimization goal into executable sub-tasks of each sub-unit, and collect the local optimization results of each sub-unit simultaneously and summarize them at the global level.

5. The system according to claim 1, characterized in that, The specific process of the model predictive control to perform global short-term deterministic optimization is as follows: based on historical operating data and digital twin model simulation results, predict the system load demand and photovoltaic output in the short term; with system power balance and optimal operating efficiency as the core objectives, solve for the global-level scheduling scheme.

6. The system according to claim 1, characterized in that, The specific process of multi-agent reinforcement learning to regulate the optimization of distributed entities is as follows: intelligent agents are constructed for each distributed entity according to source, network, load and storage, and local optimization goals and interaction rules are set for each intelligent agent; using historical operation data and real-time status data as learning samples, each intelligent agent outputs the optimal regulation strategy under different scenarios through interactive learning; and the distributed entities are dynamically optimized and regulated through collaborative interaction among intelligent agents.

7. The system according to claim 1, characterized in that, The specific process of designing the multi-dimensional adaptive reward mechanism is as follows: The system includes four key indicators: photovoltaic grid integration rate, operational economy, grid stability, and carbon emission reduction benefits, forming a multi-dimensional evaluation system. Analyze the importance of various indicators under different operating scenarios and establish dynamic adjustment rules for indicator weights; Based on the control effects and indicator achievement of each agent, the reward value calculated quantitatively is used to provide positive feedback on the agent control behavior that conforms to the global optimization direction, and to correct the agent control strategy that deviates from the global optimization direction.

8. The system according to claim 1, characterized in that, The specific process of semantic annotation and association mapping of environmental equipment operation data and production planning business data is as follows: the environmental equipment operation data is annotated with three types of semantic information: data source, collection time, and data type; the production planning business data is annotated with three types of semantic information: planning cycle, load type, and production node; and mapping rules between the two types of data are established based on business logic and data association relationship.

9. The system according to claim 1, characterized in that, The specific process of the reverse constraint photovoltaic output and energy storage charging and discharging plan is as follows: based on the adjusted production plan and load decomposition results, analyze the current carrying capacity and operating constraints of the power grid; Based on the system operating limits obtained from digital twin model simulation, a reasonable range for photovoltaic power output and upper limits for energy storage charging and discharging power, as well as time restrictions, are preset; The constraints are embedded as core limitations in the planning process for photovoltaic power output and energy storage charging and discharging.

10. The system according to claim 1, characterized in that, The specific process for forming the production load breakdown plan is as follows: Based on the preprocessed real-time measurement data and the simulation results of the digital twin model, the initial production plan was adjusted; The adjusted production plan is broken down into load tasks corresponding to each entity by dividing the load decomposition dimensions. Simultaneously retrieve the reasonable range of photovoltaic output and the energy storage charging and discharging limits determined by the reverse constraints to verify the feasibility of each load task; Prioritize and allocate resources to the load tasks that pass the verification, and summarize them to form a formal production load decomposition plan.

11. The system according to claim 1, characterized in that, The specific process of simulating the scheduling effect of different trading strategies using the digital twin model is as follows: preset the green electricity trading market trading strategies, and clarify the three core parameters of each strategy: trading period, bidding method, and trading volume; input the different trading strategy parameters and the current system operation data into the digital twin model to simulate the energy flow path, supply and demand balance and economic benefits under each strategy; and output the quantitative evaluation results of the scheduling effect of each strategy.

12. The system according to claim 1, characterized in that, The specific process for generating a coordinated green electricity trading and energy dispatch scheme is as follows: The simulation evaluation results of different trading strategies are summarized, and combined with feedback information on system regulation potential, trading strategies that are both economical and feasible are selected; the selected trading strategies are combined with the source-grid-load-storage dispatch requirements, integrating three categories: photovoltaic consumption plan, energy storage charging and discharging strategy, and flexible load adjustment scheme; and the results of carbon emission reduction quantification are incorporated for comprehensive optimization.

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