Virtual power plant integrated management system

Abstract

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

Description

Technical Field

[0001] This invention relates to the field of power plant data processing technology, and in particular to a virtual power plant integrated management system. Background Technology

[0002] Virtual power plant integrated management systems involve the efficient aggregation and optimized regulation of power systems and distributed energy resources. Existing virtual power plants, in integrating distributed resources, often face problems such as insufficient accuracy in resource modeling and delayed collaborative response due to the heterogeneity of power generation equipment, energy storage units, and load terminals, their wide geographical distribution, and the dynamic characteristics of the power network. In existing technologies, source-load prediction models largely rely on existing statistical methods, which are difficult to adapt to the strong fluctuations in renewable energy output and load demand, resulting in limited grid regulation margins. Simultaneously, the aggregation of virtual machine groups for industrial scenarios lacks effective dynamic simulation methods, failing to accurately characterize resource interaction features across multiple time scales, thus hindering the formulation and implementation of demand response strategies. How to achieve real-time perception, accurate prediction, and collaborative optimization of massive distributed resources has become a key technological bottleneck for improving the operational efficiency of virtual power plants. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a virtual power plant integrated management system. This system solves the problems that virtual power plant integrated management systems need to address, such as insufficient modeling accuracy due to the heterogeneity, wide distribution, and dynamic characteristics of distributed energy resources, which leads to delayed collaborative response; insufficient adaptability of existing source-load prediction models to strong fluctuations in new energy sources; and the constraints imposed by the lack of dynamic simulation methods on demand response strategies, making it difficult to achieve efficient aggregation and optimized operation of massive distributed resources.

[0004] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows: This invention provides a virtual power plant load forecasting and dynamic regulation optimization system, comprising: The data acquisition module is used to acquire the operating parameters and communication protocol characteristics of energy equipment in real time. The operating parameters include voltage fluctuation characteristics and power ramp-up rate. The equipment fingerprint database construction module receives the operating parameters from the data acquisition module, parses the communication protocol and output characteristic parameters of the equipment, and generates the equipment fingerprint code, including the dynamic response time delay coefficient and the quantitative value of the adjustment margin. The dynamic clustering module, based on the dynamic response time delay coefficient in the device fingerprint code, combined with the geographical coordinates of the energy equipment and the power regulation rate threshold, uses a spatial clustering algorithm to generate regional simulation sub-models including regional topological constraints; The multi-timescale prediction module inputs the irradiance and wind speed gradient parameters from the regional simulation sub-model and meteorological monitoring data into the spatiotemporal graph convolutional network to generate source load prediction data with multiple time granularities. The digital twin simulation module receives multi-time-granularity source-load prediction data and generates a dynamic response strategy by coupling electromagnetic transient simulation and quasi-steady-state simulation. The dynamic response strategy includes energy storage charging and discharging thresholds and demand response priorities. The wide-area control module generates a sequence of scheduling instructions, including communication interruption compensation instructions and edge node reinforcement learning models, based on the energy storage charging and discharging thresholds and a time slot allocation algorithm. The cross-domain collaboration module, based on multi-time granularity source-load prediction data and scheduling instruction sequences, verifies data sharing permissions through a dual-chain blockchain architecture and generates federated learning feature parameters required for zero-knowledge proofs. The multi-energy flow regulation module constructs an energy efficiency conversion model between the power-to-hydrogen device and the thermal storage tank based on the power curtailment risk prediction results in the dynamic response strategy, and outputs a multi-energy flow collaborative control strategy that includes virtual capacity mapping parameters for hydrogen energy storage.

[0005] Furthermore, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention also includes: The device fingerprint database construction module transmits the dynamic response time delay coefficient in the generated device fingerprint code to the dynamic clustering module, and transmits the adjustment margin quantification value in the device fingerprint code to the multi-time scale prediction module; The dynamic clustering module, based on the received dynamic response time delay coefficient, combined with the geographical coordinates of the energy equipment and the power regulation rate threshold, triggers the region division process in the spatial clustering algorithm; The multi-timescale prediction module adjusts the input weight allocation associated with regional topological constraints in the spatiotemporal graph convolutional network based on the received adjustment margin quantification.

[0006] Furthermore, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention also includes: The regional topology constraint parameters and power regulation rate thresholds included in the regional simulation sub-model generated by the dynamic clustering module; The regional simulation sub-models are synchronously transmitted to the digital twin simulation module and the multi-energy flow regulation module; The digital twin simulation module divides the boundary conditions of the equipment group for electromagnetic transient simulation based on the regional topology constraint parameters in the regional simulation sub-model. The multi-energy flow regulation module extracts the power regulation rate threshold from the regional simulation sub-model as a dynamic constraint parameter for constructing the energy efficiency conversion model of the electro-hydrogen conversion device and the thermal storage tank.

[0007] Furthermore, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention also includes: The multi-time-scale source load prediction data generated by the multi-time-scale prediction module includes power ramp features transformed from the power ramp rate parameters collected by the data acquisition module. Multi-time granularity source load prediction data are pushed in parallel to the digital twin simulation module and the cross-domain collaboration module; The digital twin simulation module generates power curtailment risk prediction results based on the power ramp-up characteristics in the received source-load prediction data and the equipment group boundary conditions of the regional simulation sub-model. The cross-domain collaboration module extracts timestamp information from the source load prediction data and performs time-series alignment verification with the energy equipment transaction records stored in the dual-chain blockchain architecture.

[0008] Furthermore, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention also includes: The dynamic response strategy generated by the digital twin simulation module includes energy storage charging and discharging thresholds and demand response priority parameters. The dynamic response strategy sends energy storage charging and discharging threshold update instructions to the multi-energy flow regulation module through the wide-area control module, and transmits the demand response priority parameters to the federated learning feature parameter pool of the cross-domain collaborative module. Based on the predicted results of power curtailment risk, the wide-area control module generates update instructions that match the energy storage charging and discharging thresholds; The cross-domain collaboration module triggers a pre-defined smart contract verification process in the blockchain architecture based on the received demand response priority parameters to authorize access to the federated learning feature parameter pool.

[0009] Furthermore, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention also includes: The scheduling instruction sequence output by the wide-area control module includes communication interruption compensation instructions generated based on energy storage charging and discharging thresholds, as well as edge node reinforcement learning model parameters extracted after authorization from the federated learning feature parameter pool; The cross-domain collaboration module decomposes the scheduling instruction sequence into communication interruption compensation instructions and reinforcement learning model parameters. The communication interruption compensation instructions are pushed to the multi-energy flow regulation module to reversely correct the dynamic constraint parameters in the thermal storage tank energy efficiency conversion model. The parameters of the reinforcement learning model are written through the sidechain storage nodes of the dual-chain blockchain architecture and bound to the transaction records after time-series alignment verification for data integrity.

[0010] Furthermore, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention also includes: Based on the authorized access permissions of the federated learning feature parameter pool, the cross-domain collaboration module completes zero-knowledge proof verification and then extracts the hydrogen energy storage virtual capacity mapping parameters from the sidechain storage node bound to data integrity. The cross-domain collaboration module sends the verified virtual capacity mapping parameters of hydrogen energy storage back to the multi-energy flow regulation module, and, in conjunction with the dynamic constraint parameters of the energy efficiency conversion model, triggers the capacity redistribution calculation between the electro-hydrogen conversion device and the thermal storage tank. Based on the capacity redistribution results, the multi-energy flow regulation module generates load transfer path planning parameters in the multi-energy flow collaborative control strategy that match the power curtailment risk prediction results.

[0011] Furthermore, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention also includes: The cross-domain collaboration module is equipped with a data trust verification unit, which receives the encrypted identification signal from the federated learning feature parameter pool; The data trust verification unit connects to the distributed ledger node of the blockchain architecture and retrieves the integrity verification code of the hydrogen energy storage virtual capacity mapping parameters. The data credibility verification unit outputs the verified hydrogen energy storage virtual capacity mapping parameters to the capacity dynamic allocation unit of the multi-energy flow regulation module. The encrypted identifier signal of the federated learning feature parameter pool triggers the data trust verification unit. The data trust verification unit completes the verification of hydrogen energy storage parameters through the blockchain distributed ledger, and the verification result is transmitted to the capacity dynamic allocation unit.

[0012] Furthermore, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention also includes... The capacity dynamic allocation unit collects real-time data on the power fluctuation of the electro-hydrogen conversion unit and the thermal inertia energy buffer index of the thermal storage tank. The capacity dynamic allocation unit analyzes the schedulable capacity threshold in the hydrogen energy storage virtual capacity mapping parameters and associates it with the energy conversion efficiency parameters of the electro-hydrogen conversion device and the thermal storage tank. The capacity dynamic allocation unit generates an electro-hydrogen conversion priority instruction and sends it to the load transfer path planning unit. The power data of the electro-hydrogen conversion unit and the buffer index of the thermal storage tank are input into the capacity dynamic allocation unit. The capacity dynamic allocation unit outputs the electro-hydrogen conversion priority instruction to the path planning unit based on the scheduling threshold and conversion efficiency parameters in the hydrogen energy storage mapping parameters.

[0013] Furthermore, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention also includes: The load transfer path planning unit receives the power curtailment risk quantification signal from the digital twin simulation module; The load transfer path planning unit compares the conflicting nodes between the priority command for the electro-hydrogen conversion and the power curtailment risk quantification signal, and reconstructs the thermal energy transmission path of the thermal storage tank and the electrolysis power allocation ratio of the electro-hydrogen conversion unit. The load transfer path planning unit feeds back a load transfer topology adapted to the current power curtailment risk level to the execution terminal; The power curtailment risk quantification signal triggers the path planning unit, which reconstructs the thermal storage tank transportation path and electrolysis power allocation through conflict node analysis, and generates risk-adaptive topology instructions to the execution terminal.

[0014] Beneficial effects of this invention; This invention improves the accuracy of dynamic characteristic modeling of distributed energy resources by collaboratively constructing equipment fingerprint encoding and regional simulation sub-models, effectively alleviating the collaborative lag problem caused by the wide-area distribution of heterogeneous equipment. Based on a multi-timescale prediction mechanism using spatiotemporal graph convolutional networks, it integrates meteorological parameters and regional topological constraints to enhance the analytical capability of source-load prediction for strong fluctuations in new energy output. Combined with digital twin simulation and coupled electromagnetic transient and quasi-steady-state analysis, it generates dynamic response strategies, providing real-time quantitative control basis for demand response. The cross-domain collaboration module utilizes a dual-chain blockchain architecture to achieve secure verification of federated learning parameters and anchoring of data integrity. The multi-energy flow regulation module optimizes load transfer paths through hydrogen energy storage virtual capacity mapping and electrothermal coupling models, ultimately forming a comprehensive virtual power plant management architecture with high accuracy in aggregating distributed resources, fast multi-energy flow collaborative response, and strong data interaction reliability. Attached Figure Description

[0015] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the accompanying drawings without creative effort.

[0016] Figure 1 This is a system architecture diagram of a virtual power plant integrated management system provided in an embodiment of the present invention.

[0017] Figure 2 Architecture diagram of the data acquisition module provided in this embodiment of the invention.

[0018] Figure 3 Architecture diagram of the device fingerprint database construction module provided in this embodiment of the invention.

[0019] Figure 4 The architecture diagram of the dynamic clustering module provided in this embodiment of the invention.

[0020] Figure 5 Architecture diagram of the multi-timescale prediction module provided in this embodiment of the invention.

[0021] Figure 6 Architecture diagram of the digital twin simulation module provided in this embodiment of the invention.

[0022] Figure 7 Architecture diagram of the wide-area control module provided in this embodiment of the invention.

[0023] Figure 8 Architecture diagram of the cross-domain collaboration module provided in this embodiment of the invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings. To better understand the objectives of this invention, it will be described in further detail below.

[0025] Please see Figures 1 to 8 This invention provides a virtual power plant load forecasting and dynamic regulation optimization system, comprising: The data acquisition module is used to acquire the operating parameters and communication protocol characteristics of energy equipment in real time. The operating parameters include voltage fluctuation characteristics and power ramp-up rate. The equipment fingerprint database construction module receives the operating parameters from the data acquisition module, parses the communication protocol and output characteristic parameters of the equipment, and generates the equipment fingerprint code, including the dynamic response time delay coefficient and the quantitative value of the adjustment margin. The dynamic clustering module, based on the dynamic response time delay coefficient in the device fingerprint code, combined with the geographical coordinates of the energy equipment and the power regulation rate threshold, uses a spatial clustering algorithm to generate regional simulation sub-models including regional topological constraints; The multi-timescale prediction module inputs the irradiance and wind speed gradient parameters from the regional simulation sub-model and meteorological monitoring data into the spatiotemporal graph convolutional network to generate source load prediction data with multiple time granularities. The digital twin simulation module receives multi-time-granularity source-load prediction data and generates a dynamic response strategy by coupling electromagnetic transient simulation and quasi-steady-state simulation. The dynamic response strategy includes energy storage charging and discharging thresholds and demand response priorities. The wide-area control module generates a sequence of scheduling instructions, including communication interruption compensation instructions and edge node reinforcement learning models, based on the energy storage charging and discharging thresholds and a time slot allocation algorithm. The cross-domain collaboration module, based on multi-time granularity source-load prediction data and scheduling instruction sequences, verifies data sharing permissions through a dual-chain blockchain architecture and generates federated learning feature parameters required for zero-knowledge proofs. The multi-energy flow regulation module constructs an energy efficiency conversion model between the power-to-hydrogen device and the thermal storage tank based on the power curtailment risk prediction results in the dynamic response strategy, and outputs a multi-energy flow collaborative control strategy that includes virtual capacity mapping parameters for hydrogen energy storage.

[0026] The virtual power plant load forecasting and dynamic regulation optimization system of this invention achieves refined management and control of distributed energy resources through a hierarchical architecture. The data acquisition module is deployed at the energy equipment site, acquiring voltage fluctuation characteristics and power ramp-up rate parameters in real time through an embedded sensor network, and analyzing communication protocol characteristics to obtain raw equipment operating status data. These parameters directly reflect the equipment's dynamic response capability, providing basic input for subsequent processing. The output of this module is transmitted to the equipment fingerprint database construction module, forming the starting point of the system data flow.

[0027] The equipment fingerprint database construction module receives operating parameters from the data acquisition module and uses feature extraction algorithms to parse the communication protocol and output characteristic parameters. The module generates a dynamic response delay coefficient by quantifying the equipment response latency and calculates an adjustment margin quantification value based on the upper and lower limits of output, constructing a unique equipment fingerprint code. This equipment fingerprint code digitizes the dynamic characteristics of the equipment, serving as the basis for subsequent region division and prediction. The dynamic response delay coefficient of this module is transmitted to the dynamic clustering module, while the adjustment margin quantification value is input to the multi-timescale prediction module, achieving the segmentation and processing of equipment features.

[0028] The dynamic clustering module integrates the geographical coordinates of energy devices and power regulation rate threshold parameters based on the dynamic response time delay coefficient in the device fingerprint encoding. The module applies spatial clustering algorithms such as DBSCAN to divide regional topological constraints according to device time delay differences and geographical proximity, generating regional simulation sub-models. These sub-models include power regulation rate thresholds and electrical boundary parameters, ensuring consistency of device groups in both physical space and dynamic characteristics. This model is synchronously transmitted to the digital twin simulation module and the multi-energy flow regulation module, providing regionalized input for simulation and multi-energy flow optimization.

[0029] The multi-timescale prediction module inputs irradiance and wind speed gradient parameters from regional simulation sub-models and meteorological monitoring data into a spatiotemporal graph convolutional network. The module utilizes graph convolutional layers to capture regional topological constraints and processes power ramp-up characteristics through time series analysis, outputting source-load prediction data at multiple time granularities. This data includes the transformed power ramp-up characteristics, reflecting energy supply and demand trends at different time scales. This data is pushed in parallel to the digital twin simulation module and the cross-domain collaboration module, supporting simulation risk analysis and data verification processes.

[0030] The digital twin simulation module receives multi-time-granularity source-load prediction data and couples electromagnetic transient simulation and quasi-steady-state simulation processes based on the boundary conditions of the equipment group in the regional simulation sub-model. The module simulates the microsecond-level dynamic interaction of the equipment group through electromagnetic transient simulation, while the quasi-steady-state simulation analyzes the minute-level energy balance, generating a dynamic response strategy. The dynamic response strategy includes energy storage charging and discharging thresholds and demand response priority parameters. The former is dynamically set based on the predicted results of power curtailment risk, while the latter prioritizes load adjustability through a multi-attribute decision algorithm. After the strategy is output, it is distributed through the wide-area control module and the cross-domain collaboration module, forming the basis for control commands.

[0031] The wide-area control module generates a scheduling instruction sequence based on the energy storage charging and discharging thresholds output by the digital twin simulation module, using a time slot allocation algorithm. This sequence includes communication interruption compensation instructions and edge node reinforcement learning model parameters; the former compensates for data loss caused by network anomalies, while the latter is extracted from the federated learning feature parameter pool. The module transmits the instruction sequence to the cross-domain collaboration module for decomposition and binding, ensuring the executability of the scheduling strategy. The output of this module drives model correction in the multi-energy flow regulation module, maintaining the system's real-time response.

[0032] The cross-domain collaboration module, based on multi-time-granularity source-load prediction data and scheduling instruction sequences, verifies data sharing permissions through the main chain of a dual-chain blockchain architecture. The module extracts timestamps from the source-load prediction data and performs time-series alignment verification with energy equipment transaction records stored on the blockchain to ensure data integrity. Simultaneously, the module decomposes the scheduling instruction sequence into communication interruption compensation instructions and reinforcement learning model parameters. The former is pushed to the multi-energy flow regulation module for model correction, while the latter is written to the blockchain sidechain node and bound to the verification record. This module generates the federated learning feature parameters required for zero-knowledge proofs, achieving secure cross-domain collaboration.

[0033] The multi-energy flow regulation module receives the power curtailment risk prediction results from the dynamic response strategy and constructs an energy efficiency conversion model for the electricity-to-hydrogen (EHH) unit and the thermal storage tank. The module extracts the power regulation rate threshold from the regional simulation sub-model as a dynamic constraint parameter and receives the hydrogen storage virtual capacity mapping parameters returned by the cross-domain collaboration module, triggering capacity reallocation calculations for the EHH unit and the thermal storage tank. The output includes a multi-energy flow collaborative control strategy with hydrogen storage virtual capacity mapping parameters, generates load transfer path planning parameters, and optimizes the EHH / thermal multi-energy flow conversion path. This strategy ultimately achieves resource aggregation and response optimization, resulting in a closed-loop system processing flow.

[0034] Specifically, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention further includes: The device fingerprint database construction module transmits the dynamic response time delay coefficient in the generated device fingerprint code to the dynamic clustering module, and transmits the adjustment margin quantification value in the device fingerprint code to the multi-time scale prediction module; The dynamic clustering module, based on the received dynamic response time delay coefficient, combined with the geographical coordinates of the energy equipment and the power regulation rate threshold, triggers the region division process in the spatial clustering algorithm; The multi-timescale prediction module adjusts the input weight allocation associated with regional topological constraints in the spatiotemporal graph convolutional network based on the received adjustment margin quantification.

[0035] After generating the device fingerprint code, the device fingerprint database construction module executes data-directed transmission logic. This module, through its internal data processing unit, encapsulates the dynamic response delay coefficient and the adjustment margin quantification value into independent data streams. The dynamic response delay coefficient characterizes the device's command response latency, while the adjustment margin quantification value reflects the device's adjustable output range. Utilizing communication interface protocols, such as TCP / IP-based packet transmission mechanisms, the device fingerprint database construction module directionally sends the dynamic response delay coefficient to the dynamic clustering module, while simultaneously transmitting the adjustment margin quantification value to the multi-timescale prediction module. This process achieves differentiated routing of device characteristic parameters, providing a specialized input foundation for subsequent modules.

[0036] The dynamic clustering module receives the dynamic response time delay coefficients transmitted from the device fingerprint database construction module. Combining this with the geographical coordinates of the energy devices and the power regulation rate threshold, it activates the regional division process of the spatial clustering algorithm. The module employs a density-based clustering method, such as a variant of the DBSCAN algorithm, to define the topological boundary conditions of the device groups based on the similarity parameters of the dynamic response time delay coefficients and the spatial proximity of the geographical coordinates. The power regulation rate threshold serves as the criterion for determining the core clustering points, constraining the dynamic interaction range of devices within the group, thereby generating a regional simulation sub-model that includes the electrical distance radius. The regional division process outputs the regional simulation sub-model, which integrates the regional topological constraint parameters for subsequent simulation and optimization processing.

[0037] The multi-timescale prediction module receives adjustment margin quantification values ​​transmitted from the device fingerprint database construction module, and uses these values ​​to adjust the input weight allocation mechanism of the spatiotemporal graph convolutional network. The module sets up a weight allocation layer within the spatiotemporal graph convolutional network, adjusting the margin quantification values ​​as dynamic weight factors to influence the connection weights in network nodes that are associated with regional topological constraints. This adjustment process enhances the feature influence of device nodes with high adjustment margins and optimizes the feature extraction accuracy of the graph convolutional layer for regional topological structures. The network processes regional simulation sub-models and meteorological parameters, outputting source load prediction data. This logic ensures the adaptability of the prediction model to device adjustment capabilities and supports multi-timescale analysis.

[0038] Specifically, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention further includes: The regional topology constraint parameters and power regulation rate thresholds included in the regional simulation sub-model generated by the dynamic clustering module; The regional simulation sub-models are synchronously transmitted to the digital twin simulation module and the multi-energy flow regulation module; The digital twin simulation module divides the boundary conditions of the equipment group for electromagnetic transient simulation based on the regional topology constraint parameters in the regional simulation sub-model. The multi-energy flow regulation module extracts the power regulation rate threshold from the regional simulation sub-model as a dynamic constraint parameter for constructing the energy efficiency conversion model of the electro-hydrogen conversion device and the thermal storage tank.

[0039] The dynamic clustering module applies spatial clustering algorithms to process the dynamic response time delay coefficients and geographical coordinate parameters of energy equipment when generating regional simulation sub-models. The module employs density clustering methods, such as a variant of DBSCAN, to define group boundary conditions based on the geographical proximity of equipment and power regulation rate thresholds, outputting simulation sub-models that include regional topology constraint parameters. These regional topology constraint parameters characterize the electrical distance boundaries of equipment groups, and the power regulation rate threshold reflects the rate limit of equipment output changes. This model serves as the basis for regionalized data processing of the system. The module generates regional simulation sub-models in real time through internal data pipelines, providing structured input for subsequent simulations and optimizations.

[0040] The regional simulation sub-models are synchronously transmitted to the digital twin simulation module and the multi-energy flow regulation module via the system data bus mechanism. The modules utilize message queuing protocols such as AMQP to achieve real-time distribution of model data, ensuring that the digital twin simulation module and the multi-energy flow regulation module receive the same input simultaneously. This transmission logic supports parallel processing, avoids data latency, and enables the two modules to execute their respective functions based on a unified regional model, enhancing system collaboration efficiency. The data synchronization mechanism of the regional simulation sub-models provides a consistent framework for subsequent steps, maintaining the continuity of the processing flow.

[0041] After receiving the regional simulation sub-model, the digital twin simulation module delineates the boundary conditions of the equipment groups for electromagnetic transient simulation based on the regional topological constraint parameters. The module parses the regional topological constraint parameters, such as the spatial association rules of the equipment groups, sets the initial boundary parameters for the electromagnetic transient simulation, and simulates the microsecond-level dynamic interaction process between the equipment. This delineation process defines the simulation range of the equipment groups, constrains the physical boundaries of the electromagnetic coupling effect analysis, and provides accurate equipment group inputs for the generation of dynamic response strategies. Logically, this step links the regionalized model with the simulation execution, enhancing the physical reliability of the simulation results.

[0042] The multi-energy flow regulation module extracts the power regulation rate threshold from the regional simulation sub-model and uses it as a dynamic constraint parameter for constructing the energy efficiency conversion model of the electro-hydrogen conversion unit and the thermal storage tank. The module obtains the power regulation rate threshold through a data parsing interface; this parameter defines the dynamic range of the electrolysis efficiency model of the electro-hydrogen conversion unit and the heat loss model of the thermal storage tank. When constructing the energy efficiency conversion model, the power regulation rate threshold constrains the rate limit of energy conversion, optimizing the adaptability of the multi-energy flow collaborative control strategy. This process maps equipment regulation capabilities to the multi-energy flow model, supports load transfer path planning, and forms the system's closed-loop optimization logic.

[0043] Specifically, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention further includes: The multi-time-scale source load prediction data generated by the multi-time-scale prediction module includes power ramp features transformed from the power ramp rate parameters collected by the data acquisition module. Multi-time granularity source load prediction data are pushed in parallel to the digital twin simulation module and the cross-domain collaboration module; The digital twin simulation module generates power curtailment risk prediction results based on the power ramp-up characteristics in the received source-load prediction data and the equipment group boundary conditions of the regional simulation sub-model. The cross-domain collaboration module extracts timestamp information from the source load prediction data and performs time-series alignment verification with the energy equipment transaction records stored in the dual-chain blockchain architecture.

[0044] When generating source-load prediction data at multiple time scales, the multi-time-scale prediction module transforms the power ramp-up rate parameters provided by the data acquisition module into power ramp-up features using a feature conversion algorithm. This module applies signal processing techniques such as discrete wavelet transform to extract the trend parameters within the power ramp-up rate, forming power ramp-up features that characterize the fluctuations in equipment output. Integrating these features into the source-load prediction data reflects the dynamic changes in energy supply and demand at different time scales, providing a foundational input for subsequent analysis. The multi-time-scale source-load prediction data is output through the module's internal data conversion unit, supporting the system's multi-level processing flow.

[0045] After multi-time-granularity source load prediction data is generated, it is pushed in parallel to the digital twin simulation module and the cross-domain collaboration module. This push mechanism uses a distributed messaging protocol such as Kafka message queue to achieve real-time distribution of the data stream, ensuring that both modules receive the same input synchronously. Parallel processing logic avoids data latency, maintains system response efficiency, and provides a consistent data source for risk analysis in the digital twin simulation module and data verification in the cross-domain collaboration module. This process strengthens inter-module collaboration and forms a key node in the system data flow.

[0046] The digital twin simulation module receives power ramp-up characteristics from source-load prediction data and, combined with the equipment group boundary conditions defined by the regional simulation sub-model, generates curtailment risk prediction results. The module couples electromagnetic transient simulation and quasi-steady-state simulation techniques, applying probabilistic models such as Monte Carlo simulation to analyze the dynamic matching degree between power ramp-up characteristics and equipment groups. Equipment group boundary conditions constrain the simulation range and define electrical interaction rules between equipment, thereby outputting curtailment risk prediction results based on regional differences. This result quantifies the probability of curtailment, providing a decision-making basis for dynamic response strategies and logically achieving a closed loop between risk warning and simulation execution.

[0047] The cross-domain collaboration module extracts timestamp information from the source-load prediction data and performs time-series alignment verification with energy device transaction records stored in the dual-chain blockchain architecture. The module employs time-series matching algorithms such as dynamic time warping to align the timestamps with the transaction records on the main blockchain, verifying data consistency. This process is executed through smart contracts, generating the federated learning feature parameters required for zero-knowledge proofs, ensuring data integrity and the credibility of cross-domain interactions. The time-series alignment verification logically links prediction data and transaction behavior, supporting secure system collaboration.

[0048] Specifically, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention further includes: The dynamic response strategy generated by the digital twin simulation module includes energy storage charging and discharging thresholds and demand response priority parameters. The dynamic response strategy sends energy storage charging and discharging threshold update instructions to the multi-energy flow regulation module through the wide-area control module, and transmits the demand response priority parameters to the federated learning feature parameter pool of the cross-domain collaborative module. Based on the predicted results of power curtailment risk, the wide-area control module generates update instructions that match the energy storage charging and discharging thresholds; The cross-domain collaboration module triggers a pre-defined smart contract verification process in the blockchain architecture based on the received demand response priority parameters to authorize access to the federated learning feature parameter pool.

[0049] When generating a dynamic response strategy, the digital twin simulation module outputs energy storage charging and discharging thresholds and demand response priority parameters based on the results of coupled electromagnetic transient simulation and quasi-steady-state simulation. The energy storage charging and discharging thresholds are set using a dynamic programming algorithm, reflecting the charging and discharging boundary limitations of the equipment group. The demand response priority parameters apply a multi-attribute decision algorithm to prioritize load adjustability, such as based on load interruption cost and response speed. This strategy provides a quantitative basis for system control and supports the execution input of subsequent modules. After the dynamic response strategy is generated, it is transmitted to the wide-area control module via the internal data bus, forming the distribution starting point.

[0050] After receiving the dynamic response strategy, the wide-area control module executes the instruction distribution logic. This module encapsulates the energy storage charging and discharging thresholds into update instructions and sends them to the multi-energy flow regulation module via communication protocols such as Modbus RTU to adjust the operating parameters of the energy storage equipment. Simultaneously, demand response priority parameters are transmitted via a data interface to the federated learning feature parameter pool of the cross-domain collaboration module, serving as part of the feature data. This distribution process enables targeted processing of strategy parameters, ensuring that different modules receive specialized inputs and supporting system collaborative optimization. The output of the wide-area control module maintains the consistency of strategy execution, avoiding data latency issues.

[0051] The wide-area control module generates update instructions that match the energy storage charging and discharging thresholds based on the curtailment risk prediction results provided by the digital twin simulation module. The module applies a time-slot allocation algorithm to process the risk prediction results, such as mapping the curtailment probability to charging and discharging power adjustment amounts, to generate the instruction content. The update instructions include specific adjustment values ​​for the charging and discharging thresholds, such as the offset of the charging and discharging power upper limit, driving the multi-energy flow regulation module to correct the energy storage model. This process enables dynamic adaptation of the energy storage strategy to the risk level, optimizes system response efficiency, and logically links risk analysis and control execution.

[0052] The cross-domain collaboration module triggers a pre-defined smart contract verification process within the blockchain architecture based on the received demand response priority parameters to authorize access to the federated learning feature parameter pool. The module executes the smart contract through the main chain of the dual-chain blockchain, such as using the Hyperledger Fabric framework, to verify parameter permissions. The demand response priority parameters serve as input, driving contract condition judgments and authorizing read and write permissions to the feature parameter pool. This verification process generates the federated learning feature parameters required for zero-knowledge proofs, ensuring the security of data sharing. Logically, this step achieves trusted collaboration between access control and data interaction, forming a closed-loop system security mechanism. After outputting the authorization result, the module sends it back to the wide-area control module or the multi-energy flow regulation module to complete the overall processing flow.

[0053] Specifically, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention further includes: The scheduling instruction sequence output by the wide-area control module includes communication interruption compensation instructions generated based on energy storage charging and discharging thresholds, as well as edge node reinforcement learning model parameters extracted after authorization from the federated learning feature parameter pool; The cross-domain collaboration module decomposes the scheduling instruction sequence into communication interruption compensation instructions and reinforcement learning model parameters. The communication interruption compensation instructions are pushed to the multi-energy flow regulation module to reversely correct the dynamic constraint parameters in the thermal storage tank energy efficiency conversion model. The parameters of the reinforcement learning model are written through the sidechain storage nodes of the dual-chain blockchain architecture and bound to the transaction records after time-series alignment verification for data integrity.

[0054] When outputting the scheduling instruction sequence, the wide-area control module generates communication interruption compensation instructions based on the energy storage charging and discharging thresholds, and extracts edge node reinforcement learning model parameters through the authorization mechanism of the federated learning feature parameter pool. This module applies a time slot allocation algorithm to process the energy storage charging and discharging thresholds, mapping the threshold parameters to power compensation logic under communication interruption scenarios to generate instruction content. Simultaneously, the module uses the access control interface to call the authorization results of the federated learning feature parameter pool, extracting edge node reinforcement learning model parameters to form a serialized data stream including instructions and parameters. The scheduling instruction sequence is transmitted to the cross-domain collaboration module via the data bus, realizing the encapsulation and distribution of control strategies and supporting subsequent collaborative optimization processes. This process achieves dynamic generation of control strategies and integration of authorized data, logically linking energy storage management and communication anomaly handling.

[0055] After receiving the scheduling instruction sequence, the cross-domain collaboration module executes instruction decomposition logic, separating the sequence into two independent data units: communication interruption compensation instructions and reinforcement learning model parameters. The module uses a message parsing protocol such as JSON data format for processing, classifying and extracting data based on instruction type identifiers. The communication interruption compensation instructions are transmitted to the multi-energy flow regulation module via a push mechanism, while the reinforcement learning model parameters are retained for blockchain writing. This decomposition is implemented through an internal processor, ensuring separate processing of instructions and parameters, avoiding data conflicts, and logically establishing a basic framework for cross-module collaboration, supporting targeted data flow transmission.

[0056] After the communication interruption compensation command is pushed to the multi-energy flow regulation module, it is used to reversely correct the dynamic constraint parameters in the thermal storage tank's energy efficiency conversion model. The module receives the command content, such as the compensation power value, and applies a model correction algorithm to adjust the input constraints of the thermal storage tank's energy efficiency conversion model. The correction process considers the thermal inertia characteristics of the thermal storage tank, iteratively optimizing and updating the model parameters to adapt the energy efficiency conversion model to power fluctuations caused by communication interruptions. This step achieves dynamic adaptation between the command and the model, logically strengthening the system's robustness to communication anomalies and providing stable input for the multi-energy flow collaborative control strategy.

[0057] Reinforcement learning model parameters are written to sidechain storage nodes in a dual-chain blockchain architecture and bound to time-aligned and verified transaction records for data integrity. The cross-domain collaboration module calls blockchain interfaces, such as those based on the Hyperledger framework, to write parameters to the sidechain storage nodes. The module applies a hash algorithm to generate a digest value for each transaction record, which is then associated with the reinforcement learning model parameters to ensure immutable data storage. This binding process is executed through a smart contract, logically guaranteeing the security and traceability of the parameters, forming a closed-loop data management mechanism to support subsequent calls to the federated learning feature parameter pool. After outputting the binding results, the module completes the overall data flow processing and sends the data back to other modules in the system for collaborative optimization.

[0058] Specifically, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention further includes: Based on the authorized access permissions of the federated learning feature parameter pool, the cross-domain collaboration module completes zero-knowledge proof verification and then extracts the hydrogen energy storage virtual capacity mapping parameters from the sidechain storage node bound to data integrity. The cross-domain collaboration module sends the verified virtual capacity mapping parameters of hydrogen energy storage back to the multi-energy flow regulation module, and, in conjunction with the dynamic constraint parameters of the energy efficiency conversion model, triggers the capacity redistribution calculation between the electro-hydrogen conversion device and the thermal storage tank. Based on the capacity redistribution results, the multi-energy flow regulation module generates load transfer path planning parameters in the multi-energy flow collaborative control strategy that match the power curtailment risk prediction results.

[0059] After obtaining authorized access to the federated learning feature parameter pool, the cross-domain collaboration module executes a zero-knowledge proof verification process to extract the hydrogen storage virtual capacity mapping parameters. This module invokes a zero-knowledge proof protocol based on a dual-chain blockchain architecture, such as the zk-SNARK verification mechanism, to verify the validity of data integrity bindings in the sidechain storage nodes without exposing the original data. Upon successful verification, the module accesses the sidechain storage nodes through the blockchain interface and retrieves the hydrogen storage virtual capacity mapping parameters based on the data hash index. These parameters characterize the equivalent energy storage capacity relationship between electrical and hydrogen energy conversion, logically implementing a secure and reliable data acquisition process. The verification process is automatically executed through a smart contract, maintaining the immutability of data interaction.

[0060] The cross-domain collaboration module transmits the verified virtual capacity mapping parameters of hydrogen storage to the multi-energy flow regulation module via a data backhaul channel. The module employs secure transmission protocols such as TLS encrypted links to collaboratively input these parameters with the dynamic constraint parameters of the energy efficiency conversion model in the multi-energy flow regulation module. These dynamic constraint parameters, derived from the power regulation rate thresholds of the regional simulation sub-models, define the rate boundaries of energy conversion. The module triggers a capacity reallocation calculation algorithm, such as a constraint-optimized resource allocation model, to redistribute the capacity ratio between the electro-hydrogen conversion unit and the thermal storage tank. This calculation process enables dynamic adaptation and adjustment of multi-energy flow resources, supporting system operation optimization.

[0061] The multi-energy flow regulation module generates load transfer path planning parameters based on the capacity redistribution calculation results and the curtailment risk prediction results generated by the digital twin simulation module. The module applies a multi-objective optimization algorithm, using the curtailment risk level as a constraint, to construct the optimal conversion path in the electricity-hydrogen-heat multi-energy flow network. The load transfer path planning parameters define the transmission path and proportional relationship for the conversion of electrical energy to hydrogen or heat energy, logically forming a collaborative control strategy linked to the risk prediction results. These parameters, as an output component of the multi-energy flow collaborative control strategy, achieve global optimal allocation of system resources. Finally, the strategy is sent to the energy equipment execution layer via control commands, completing a closed-loop processing flow from data analysis to equipment regulation.

[0062] Specifically, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention further includes: The cross-domain collaboration module is equipped with a data trust verification unit, which receives the encrypted identification signal from the federated learning feature parameter pool; The data trust verification unit connects to the distributed ledger node of the blockchain architecture and retrieves the integrity verification code of the hydrogen energy storage virtual capacity mapping parameters. The data credibility verification unit outputs the verified hydrogen energy storage virtual capacity mapping parameters to the capacity dynamic allocation unit of the multi-energy flow regulation module. The encrypted identifier signal of the federated learning feature parameter pool triggers the data trust verification unit. The data trust verification unit completes the verification of hydrogen energy storage parameters through the blockchain distributed ledger, and the verification result is transmitted to the capacity dynamic allocation unit.

[0063] The cross-domain collaboration module includes a data trust verification unit that receives encrypted identifier signals transmitted from the federated learning feature parameter pool. These encrypted identifier signals are generated using asymmetric encryption algorithms, such as RSA public-key cryptography, and represent authorization credentials for accessing the virtual capacity mapping parameters of hydrogen energy storage. The data trust verification unit parses the permission information in the encrypted identifier signals, triggering the blockchain architecture's verification process. This unit connects to the feature parameter pool through an internal secure communication interface, establishing an encrypted data transmission channel and providing an input basis for subsequent blockchain verification.

[0064] The data trust verification unit connects to the distributed ledger nodes of the blockchain architecture and retrieves the integrity check code of the hydrogen energy storage virtual capacity mapping parameters. The unit calls blockchain interface protocols such as the Hyperledger Fabric SDK to access the check code data stored in the distributed ledger. The integrity check code is generated using a hash algorithm, such as a SHA-256 digest value, and is used to verify whether the hydrogen energy storage virtual capacity mapping parameters have been tampered with. The unit matches the encrypted identifier signal with the check code for verification and applies a zero-knowledge proof protocol to confirm the data validity. The verification process is automatically executed through a smart contract to determine the trustworthiness of the parameters. This step establishes a collaborative verification mechanism for data integrity and permissions.

[0065] The data trust verification unit outputs the verified hydrogen energy storage virtual capacity mapping parameters to the capacity dynamic allocation unit of the multi-energy flow regulation module. The unit transmits the parameters to the data receiving interface of the capacity dynamic allocation unit via a secure transmission protocol such as TLS encrypted link. A verification result identifier, such as a digital signature, is attached during transmission for the receiving unit to perform secondary verification. This output logic enables the directed flow of verification data to the execution unit, providing a trusted input basis for capacity reallocation.

[0066] The encrypted identifier signal generated by the federated learning feature parameter pool triggers the execution process of the data trust verification unit. The feature parameter pool generates the encrypted identifier signal, such as a dynamic token based on an access control list, through a permission management interface. This signal includes a timestamp and operation permission code, driving the data trust verification unit to initiate the blockchain verification process. The triggering logic is implemented through an event-driven mechanism, such as a message queue listening mode, to maintain real-time collaboration between modules.

[0067] After verifying the hydrogen storage parameters using the blockchain distributed ledger, the data trust verification unit transmits the verification results to the capacity dynamic allocation unit. The unit generates a data packet including the verification status, such as a "verification passed / failed" status code and a timestamp, and transmits it to the capacity dynamic allocation unit via the data bus. The capacity dynamic allocation unit decides whether to enable the hydrogen storage parameters based on the verification results, logically forming a "verification-execution" closed loop. This transmission process supports the security decisions of the multi-energy flow regulation module and enables the effective transmission of blockchain verification results to the equipment control layer.

[0068] Specifically, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention also includes The capacity dynamic allocation unit collects real-time data on the power fluctuation of the electro-hydrogen conversion unit and the thermal inertia energy buffer index of the thermal storage tank. The capacity dynamic allocation unit analyzes the schedulable capacity threshold in the hydrogen energy storage virtual capacity mapping parameters and associates it with the energy conversion efficiency parameters of the electro-hydrogen conversion device and the thermal storage tank. The capacity dynamic allocation unit generates an electro-hydrogen conversion priority instruction and sends it to the load transfer path planning unit. The power data of the electro-hydrogen conversion unit and the buffer index of the thermal storage tank are input into the capacity dynamic allocation unit. The capacity dynamic allocation unit outputs the electro-hydrogen conversion priority instruction to the path planning unit based on the scheduling threshold and conversion efficiency parameters in the hydrogen energy storage mapping parameters.

[0069] The capacity dynamic allocation unit in the multi-energy flow regulation module collects real-time power fluctuation data of the electro-hydrogen conversion unit and the thermal inertia energy buffer index of the thermal storage tank via an industrial bus protocol. The power fluctuation data of the electro-hydrogen conversion unit is acquired through a sensor network, reflecting the real-time output changes in the water electrolysis hydrogen production process. The thermal inertia energy buffer index of the thermal storage tank is calculated based on temperature gradient monitoring data, characterizing the dynamic response capability of the thermal energy storage and release of the thermal storage medium. The collected data is normalized by the module's internal data preprocessing unit, providing standardized input for subsequent parameter analysis. This real-time acquisition mechanism establishes the data foundation for equipment operating status and capacity allocation decisions.

[0070] The capacity dynamic allocation unit receives the hydrogen storage virtual capacity mapping parameters verified by the cross-domain collaboration module and analyzes the schedulable capacity threshold parameter. This threshold defines the maximum schedulable capacity range of the hydrogen storage system within a specific time window. The unit uses a data association algorithm to collaboratively analyze the schedulable capacity threshold with the energy conversion efficiency parameters of the electro-hydrogen conversion device and the thermal energy conversion efficiency parameters of the thermal storage tank, constructing a multi-energy flow conversion efficiency matrix. The association logic employs a weighted allocation model to quantify the efficiency priority of different energy conversion paths, supporting subsequent decision generation. This process achieves dynamic matching between equipment physical characteristics and scheduling strategies.

[0071] The capacity dynamic allocation unit generates priority instructions for the electro-hydrogen conversion based on the energy conversion efficiency matrix and real-time acquired data. The unit applies a multi-objective optimization algorithm, using a schedulable capacity threshold as a constraint, and combines the power fluctuation characteristics of the electro-hydrogen conversion unit with the thermal inertia index of the thermal storage tank to calculate the optimal priority weights for the electricity-to-hydrogen conversion. The priority instructions include conversion path selection parameters and capacity allocation ratio coefficients, which are encapsulated into control instruction frames by an instruction encoder. These instructions are transmitted to the load transfer path planning unit to drive the generation of a multi-energy flow coordinated control strategy.

[0072] After the power fluctuation data of the electro-hydrogen conversion unit and the thermal inertia energy buffer index of the thermal storage tank are input into the capacity dynamic allocation unit, the unit executes the output process of electro-hydrogen conversion priority commands based on the schedulable capacity threshold and energy conversion efficiency parameters in the hydrogen energy storage virtual capacity mapping parameters. The unit uses dynamic scheduling algorithms, such as model predictive control, to dynamically adjust the priority command content using real-time power fluctuation data and buffer indices as feedback variables, adapting the conversion strategy to changes in equipment operating status. Finally, the commands are transmitted to the load transfer path planning unit via industrial Ethernet, forming a closed-loop logic from data acquisition to control output.

[0073] Specifically, the virtual power plant load forecasting and dynamic regulation optimization system of the present invention further includes: The load transfer path planning unit receives the power curtailment risk quantification signal from the digital twin simulation module; The load transfer path planning unit compares the conflicting nodes between the priority command for the electro-hydrogen conversion and the power curtailment risk quantification signal, and reconstructs the thermal energy transmission path of the thermal storage tank and the electrolysis power allocation ratio of the electro-hydrogen conversion unit. The load transfer path planning unit feeds back a load transfer topology adapted to the current power curtailment risk level to the execution terminal; The power curtailment risk quantification signal triggers the path planning unit, which reconstructs the thermal storage tank transportation path and electrolysis power allocation through conflict node analysis, and generates risk-adaptive topology instructions to the execution terminal.

[0074] The load transfer path planning unit in the multi-energy flow regulation module receives the curtailment risk quantification signal transmitted by the digital twin simulation module through a data interface. This signal includes regional curtailment probability level parameters and time characteristic parameters. The risk level identifier is extracted by the protocol parsing unit to provide a decision-making basis for path planning. The load transfer path planning unit sets up a signal buffer to store the quantification signal and maintain the consistency of signal processing timing.

[0075] The load transfer path planning unit establishes a conflict node analysis mechanism, comparing the spatiotemporal conflict characteristics of the priority command for the electro-hydrogen conversion and the quantification signal of power curtailment risk. The unit applies graph theory algorithms to construct a multi-energy flow network topology model, identifying overlapping nodes between the electro-hydrogen conversion path and high-risk periods of power curtailment. Based on the conflict identification results, the valve control sequence of the thermal energy transmission path of the thermal storage tank is reconstructed, and the electrolysis power allocation ratio coefficient of the electro-hydrogen conversion unit is dynamically adjusted. The reconstruction logic uses a dynamic programming model to optimize energy transmission efficiency, achieving adaptive path adjustment under risk constraints.

[0076] The load transfer path planning unit generates load transfer topology instructions adapted to the current power curtailment risk level. The unit prioritizes instruction execution based on risk level parameters and outputs instruction frames including the thermal storage tank valve opening sequence, electrolysis power allocation ratio, and timestamps. These topology instructions are transmitted to the execution terminal via industrial Ethernet, driving the electro-hydrogen conversion unit and the thermal storage tank actuators. A risk level checksum is appended to the instruction frame for the terminal to verify the match between the instruction and the risk status.

[0077] The quantification signal of power curtailment risk triggers the conflict node analysis process of the load transfer path planning unit. The time characteristic parameters in the signal activate the path reconstruction engine, which uses a spatiotemporal matching algorithm to locate energy conversion conflict nodes during periods of high power curtailment risk. The reconstruction engine outputs a topology optimization sequence for the thermal energy transmission path of the thermal storage tank and an adjustment value for the electrolysis power allocation ratio of the electricity-to-hydrogen unit, forming a risk-adaptive topology instruction. The final instruction is sent to the execution terminal through a secure communication protocol, completing the entire closed-loop process from risk perception to equipment control.

[0078] The algorithms involved in this invention are as follows: Equipment characteristic modeling algorithm: The device fingerprint database construction module uses a feature extraction algorithm to process operating parameters: Dynamic response delay coefficient: Quantifies the millisecond-level delay from command issuance to power change through time series delay analysis; Adjustment margin quantification: Based on the difference between the upper / lower limit of equipment output and the current operating point, calculate the proportion of the adjustable power range. The module identifies equipment protocol characteristics through the communication protocol parsing unit and generates a unique equipment fingerprint code.

[0079] Regional division and prediction model: The dynamic clustering module applies the spatial clustering algorithm (DBSCAN variant): Input parameters: dynamic response time delay coefficient (based on neighborhood distance threshold), geographical location coordinates (latitude and longitude), power regulation rate threshold (core point determination condition); Output: A group of devices with an electrical distance radius ≤ 5km and a time delay difference ≤ 10ms is formed into a regional simulation sub-model.

[0080] The multi-timescale prediction module employs a spatiotemporal graph convolutional network (ST-GCN): Spatial dimension: The topology of the regional simulation sub-model is used to construct graph nodes. The node features include the adjustment margin quantification value and meteorological gradient parameters. Time dimension: The power ramp rate time-dependent mechanism is captured by the gated loop unit, and the weight allocation layer dynamically adjusts the connection weights of neighboring nodes based on the adjustment margin value.

[0081] Dynamic simulation and blockchain verification: Digital twin simulation module coupled dual simulation mechanism: Electromagnetic transient simulation: The nodal admittance matrix iterative algorithm is used to simulate the electromagnetic coupling effect of equipment groups under microsecond-level power grid faults; Quasi-steady-state simulation: Output energy storage charging and discharging thresholds based on energy balance equations (solved by dynamic programming algorithm) and demand response priorities (TOPSIS multi-attribute decision ranking).

[0082] Cross-domain collaboration module deployment of dual-chain blockchain architecture: Main chain: Runs the PBFT consensus algorithm to verify data permissions and stores transaction record hash values; Sidechain: The zk-SNARK zero-knowledge proof protocol is used to verify the federated learning feature parameters, and the reinforcement learning model parameters are bound to the temporal hash values ​​of the Merkle tree structure.

[0083] Multi-energy flow collaborative optimization: The multi-energy flow regulation module constructs an energy efficiency conversion model: Input: Electrolysis efficiency of the electro-hydrogen conversion unit ≥70%, heat loss coefficient of the thermal storage tank ≤3% / h; Virtual capacity mapping for hydrogen energy storage: The conversion of electrical energy to hydrogen energy is quantified into equivalent energy storage capacity through nonlinear fitting (1MW·h electrical energy → 12kg hydrogen energy ≈ 1.2MW·h energy storage). Load transfer path planning: Dijkstra's shortest path algorithm is adopted to optimize the transmission path of multiple energy flows of electricity-hydrogen-heat with the goal of minimizing the risk of power curtailment.

[0084] Algorithm Coordination Logic: Data-driven closed loop: Power ramp-up characteristics are transmitted from the prediction module to the simulation module, driving the power curtailment risk probability model; Security verification chain: The demand response priority parameter triggers the blockchain smart contract, authorizing access to the federated learning feature parameter pool; Dynamic strategy execution: Communication interruption compensation instructions reverse the thermal inertia performance model of the thermal storage tank, and the capacity redistribution results update the electrolysis power allocation ratio in real time.

[0085] The implementation logic of each model in the technical solution of this invention is as follows: Device fingerprint modeling logic: The device fingerprint database construction module uses a feature extraction algorithm to process operating parameters: Dynamic response time delay coefficient: By analyzing the delay sequence from the issuance of historical commands to the actual change in power, a time fitting algorithm is applied to quantify the millisecond-level response characteristics of the device; Adjustment margin quantification: Based on the difference between the upper / lower limit of equipment output and the current operating point, a linear programming model is used to calculate the proportion of adjustable power range. The module identifies equipment protocol characteristics through the communication protocol parsing unit and generates a unique equipment fingerprint code.

[0086] Regional clustering and prediction models: The dynamic clustering module applies spatial clustering algorithms: Input parameters: Dynamic response time delay coefficient defines the neighborhood distance threshold, geographic location coordinates constrain spatial proximity, and power regulation rate threshold serves as the basis for core point determination; Output: Generate a group of devices with an electrical distance radius ≤ 5km and a dynamic response time delay difference ≤ 10ms, forming a regional simulation sub-model.

[0087] The multi-timescale prediction module employs a spatiotemporal graph convolutional network: Spatial topology processing: The topology of the equipment group in the regional simulation sub-model is transformed into graph nodes, and the node features are integrated to adjust the margin quantification value and meteorological gradient parameters; Temporal feature extraction: The gated loop unit processes the temporal features of the power ramp rate parameter transformation, and the weight allocation layer dynamically enhances the connection weights of nodes with high regulation capability based on the regulation margin value.

[0088] Simulation and blockchain verification mechanisms: Digital twin simulation module coupled dual simulation model: Electromagnetic transient simulation: Simulating the electromagnetic interaction of a group of devices under microsecond-level power grid faults based on the node admittance matrix iterative algorithm; Quasi-steady-state simulation: The energy balance equation is used to solve the minute-level energy storage charging and discharging thresholds, and the boundary conditions are dynamically adjusted in combination with the power curtailment risk probability model.

[0089] Cross-domain collaboration module deployment of dual-chain blockchain architecture: Main chain: Runs the PBFT consensus algorithm to verify data sharing permissions and stores transaction record hash values; Sidechains: The authenticity of federated learning feature parameters is verified through the zk-SNARK zero-knowledge proof protocol, and data integrity is ensured by binding the reinforcement learning model parameters with the temporal hash value of the Merkle tree structure.

[0090] Multi-energy flow collaborative optimization logic: The multi-energy flow regulation module constructs an energy efficiency conversion model: Virtual capacity mapping for hydrogen energy storage: Based on the electrolysis efficiency of the electro-hydrogen conversion device and the heat loss coefficient of the thermal storage tank, a nonlinear fitting algorithm is applied to quantify the electrical energy-hydrogen energy conversion relationship into an equivalent energy storage capacity. Load transfer path planning: Dijkstra's shortest path algorithm is adopted, with the goal of minimizing the risk of power curtailment, to dynamically select the optimal conversion path from electrical energy to hydrogen energy or thermal energy in the multi-energy flow network.

[0091] Model synergy: Data-driven closed loop: Power ramp-up characteristics are transmitted from the prediction module to the simulation module, driving the power curtailment risk probability model to dynamically correct the energy storage charging and discharging thresholds; Access control chain: The priority parameter of the demand response triggers the blockchain smart contract, authorizing secure access to the federated learning feature parameter pool; Dynamic response execution: The communication interruption compensation command reverses the thermal inertia performance model of the thermal storage tank, and the capacity redistribution result generates the electrolysis power allocation command in real time.

[0092] The above model achieves a technical closed loop through hierarchical data processing and verification mechanisms, enabling precise modeling of equipment characteristics, regional cluster simulation, cross-domain reliable interaction, and dynamic control of multiple energy flows.

[0093] The virtual power plant integrated management system of this invention operates in scenarios with strong fluctuations in new energy sources. It addresses the problems of insufficient modeling accuracy and response lag caused by the heterogeneity, wide distribution, and dynamic characteristics of the power system of distributed resources through a layered collaborative architecture. The system implementation process is as follows: The data acquisition module collects voltage fluctuation characteristics and power ramp-up rate parameters in real time through a sensor network deployed on energy equipment, while simultaneously analyzing communication protocol characteristics. Voltage fluctuation characteristics reflect the grid-connected stability of the equipment, and the power ramp-up rate quantifies the rate of output change, providing raw input for dynamic characteristic modeling. The equipment fingerprint database construction module receives the above parameters and uses a feature extraction algorithm to generate equipment fingerprint codes, including dynamic response time delay coefficients and quantified adjustment margin values. The dynamic response time delay coefficient quantifies the equipment response speed by analyzing historical data on the delay between command issuance and actual power changes, while the quantified adjustment margin value calculates the adjustable power range ratio based on the difference between the upper and lower limits of equipment output. This module transmits the dynamic response time delay coefficient to the dynamic clustering module, and the quantified adjustment margin value is input to the multi-timescale prediction module, achieving distributed processing of equipment characteristics.

[0094] The dynamic clustering module, based on the dynamic response time delay coefficient and combining the geographical coordinates of the equipment with the power regulation rate threshold, employs a spatial clustering algorithm to divide equipment into groups. This module defines the topological boundaries of equipment groups based on differences in equipment time delay and geographical proximity. The power regulation rate threshold serves as the criterion for determining the core clustering points, and the module outputs a regional simulation sub-model that includes regional topological constraint parameters and the power regulation rate threshold. This regional simulation sub-model is synchronously transmitted to the digital twin simulation module and the multi-energy flow regulation module via a message queue protocol, supporting parallel processing.

[0095] The multi-timescale prediction module inputs irradiance and wind speed gradient parameters from regional simulation sub-models and meteorological monitoring data into a spatiotemporal graph convolutional network. The module dynamically adjusts the weight allocation of the graph convolutional layers using adjustment margin quantification values, enhancing the impact of high adjustment margin devices on the prediction results. The network outputs multi-time-granularity source-load prediction data, integrating power ramp-up characteristics to reflect the fluctuation trends of renewable energy output and load demand. The source-load prediction data is pushed in parallel to the digital twin simulation module and the cross-domain collaboration module via a distributed message passing protocol.

[0096] The digital twin simulation module couples electromagnetic transient simulation and quasi-steady-state simulation based on the boundary conditions of the equipment group in the regional simulation sub-model. Electromagnetic transient simulation simulates the microsecond-level dynamic interactions of the equipment group, while quasi-steady-state simulation analyzes the minute-level energy balance, generating dynamic response strategies. These strategies include energy storage charging and discharging thresholds and demand response priority parameters. The former is set based on the predicted results of power curtailment risk, while the latter prioritizes load adjustability through a multi-attribute decision algorithm. The strategies are distributed to the multi-energy flow regulation module and the cross-domain coordination module via the wide-area control module.

[0097] The wide-area control module generates a sequence of scheduling instructions based on the predicted power curtailment risk, including communication interruption compensation instructions and edge node reinforcement learning model parameters. The communication interruption compensation instructions are used to compensate for data loss caused by network anomalies, and the reinforcement learning model parameters are extracted through a federated learning feature parameter pool. The sequence of scheduling instructions is then transmitted to the cross-domain collaboration module for decomposition and binding.

[0098] The cross-domain collaboration module achieves secure data collaboration through a dual-chain blockchain architecture. The module extracts timestamps from source-load prediction data and performs time-series alignment verification with energy device transaction records stored on the blockchain to ensure data consistency. The scheduling instruction sequence is decomposed into communication interruption compensation instructions and reinforcement learning model parameters. The former is pushed to the multi-energy flow regulation module for model correction, while the latter is written to the blockchain sidechain node and bound to the transaction record hash. The module securely extracts hydrogen storage virtual capacity mapping parameters from the sidechain using a zero-knowledge proof protocol and sends them back to the multi-energy flow regulation module.

[0099] The multi-energy flow regulation module constructs an energy efficiency conversion model between the electro-hydrogen conversion unit and the thermal storage tank, and performs capacity redistribution calculations based on the virtual capacity mapping parameters of hydrogen energy storage and the power regulation rate threshold. The module applies a multi-objective optimization algorithm to generate load transfer path planning parameters, using the curtailment risk level as a constraint, and constructs the optimal conversion path in the electro-hydrogen-thermal multi-energy flow network. Output control commands, including the thermal storage tank valve opening sequence and electrolysis power allocation ratio, are sent to the execution terminal via industrial Ethernet, forming a closed loop from data sensing to equipment control.

[0100] This implementation method forms a closed loop of perception-simulation-optimization-execution through digital modeling of equipment characteristics, regional dynamic clustering, multi-timescale prediction, and cross-domain blockchain collaboration. The system achieves improved resource aggregation accuracy and significantly reduced response latency; the spatiotemporal graph convolutional network integrates meteorological gradients and power ramp-up characteristics to enhance the adaptability of source-load prediction to renewable energy fluctuations; curtailment risk prediction and multi-energy flow path linkage reduce curtailment rates; and blockchain time-series verification and zero-knowledge proofs support trusted cross-domain data interaction. This architecture effectively solves the problems of insufficient resource aggregation accuracy, delayed collaborative response, and lack of dynamic simulation methods in the background technology.

[0101] This invention effectively addresses the problems of insufficient modeling accuracy and delayed collaborative response caused by the heterogeneity, wide distribution, and dynamic characteristics of power systems in distributed energy resources through a layered architecture and modular collaboration. The device fingerprint database construction module analyzes the operating parameters of energy devices in real time, generating device fingerprint codes that include dynamic response delay coefficients and quantified adjustment margin values, thus improving the accuracy of device characteristic modeling. Based on the dynamic response delay coefficients in these codes, the dynamic clustering module combines the device's geographical coordinates and power adjustment rate thresholds, applying a spatial clustering algorithm to divide regional topological constraints and form regional simulation sub-models. This model integrates the physical boundaries of device groups, reducing communication latency under wide distribution and optimizing device collaborative response. Logically, the device fingerprint codes accurately quantify the dynamic behavior of heterogeneous devices, and the spatial clustering algorithm strengthens regional grouping, alleviating model fragmentation and thus reducing response delay.

[0102] To address the shortcomings of existing source-load prediction models in adapting to strong fluctuations in renewable energy and the lack of dynamic simulation methods that restrict demand response strategies, this invention introduces a collaborative mechanism between a multi-timescale prediction module and a digital twin simulation module. The multi-timescale prediction module inputs regional simulation sub-models and irradiance and wind speed gradient parameters from meteorological monitoring data into a spatiotemporal graph convolutional network. By dynamically adjusting the margin quantification value, it dynamically adjusts the network weights and outputs multi-time-granularity source-load prediction data, including power ramp-up characteristics, enhancing the analytical capability for renewable energy output fluctuations. The digital twin simulation module receives this prediction data and couples electromagnetic transient simulation and quasi-steady-state simulation based on equipment group boundary conditions to generate dynamic response strategies such as energy storage charging and discharging thresholds and demand response priorities. Logically, the spatiotemporal graph convolutional network integrates regional topology and meteorological parameters to adapt to power output fluctuations; the coupled simulation provides multi-timescale analysis, supporting the real-time formulation of demand response strategies and overcoming the limitations of traditional models in dynamic scenarios.

[0103] To overcome the challenges of efficiently aggregating and optimizing the operation of massive, dispersed resources, this invention employs a closed-loop optimization architecture consisting of a cross-domain collaborative module and a multi-energy flow regulation module. The cross-domain collaborative module, based on multi-time-granularity source-load prediction data and scheduling instruction sequences, verifies data sharing permissions through a dual-chain blockchain architecture, performs time-series alignment verification and zero-knowledge proofs, extracts virtual capacity mapping parameters for hydrogen storage, and supports secure interaction of federated learning feature parameters. The multi-energy flow regulation module receives these parameters, combines them with the power curtailment risk prediction results in the dynamic response strategy, constructs an energy efficiency conversion model for the electricity-to-hydrogen device and the thermal storage tank, and outputs a multi-energy flow collaborative control strategy with load transfer path planning parameters. Logically, the blockchain mechanism ensures data integrity and cross-domain credibility, while the multi-energy flow model dynamically optimizes resource allocation, achieving efficient aggregation from dispersed equipment to a global strategy and improving the overall operational efficiency of the virtual power plant.

Claims

1. A virtual power plant load forecasting and dynamic regulation optimization system, characterized in that, include: The data acquisition module is used to acquire the operating parameters and communication protocol characteristics of energy equipment in real time. The operating parameters include voltage fluctuation characteristics and power ramp-up rate. The equipment fingerprint database construction module receives the operating parameters from the data acquisition module, parses the communication protocol and output characteristic parameters of the equipment, and generates the equipment fingerprint code, including the dynamic response time delay coefficient and the quantitative value of the adjustment margin. The dynamic clustering module, based on the dynamic response time delay coefficient in the device fingerprint code, combined with the geographical coordinates of the energy equipment and the power regulation rate threshold, uses a spatial clustering algorithm to generate regional simulation sub-models including regional topological constraints; The multi-timescale prediction module inputs the irradiance and wind speed gradient parameters from the regional simulation sub-model and meteorological monitoring data into the spatiotemporal graph convolutional network to generate source load prediction data with multiple time granularities. The digital twin simulation module receives multi-time-granularity source-load prediction data and generates a dynamic response strategy by coupling electromagnetic transient simulation and quasi-steady-state simulation. The dynamic response strategy includes energy storage charging and discharging thresholds and demand response priorities. The wide-area control module generates a sequence of scheduling instructions, including communication interruption compensation instructions and edge node reinforcement learning models, based on the energy storage charging and discharging thresholds and a time slot allocation algorithm. The cross-domain collaboration module, based on multi-time granularity source-load prediction data and scheduling instruction sequences, verifies data sharing permissions through a dual-chain blockchain architecture and generates federated learning feature parameters required for zero-knowledge proofs. The multi-energy flow regulation module constructs an energy efficiency conversion model between the power-to-hydrogen device and the thermal storage tank based on the power curtailment risk prediction results in the dynamic response strategy, and outputs a multi-energy flow collaborative control strategy that includes virtual capacity mapping parameters for hydrogen energy storage.

2. The virtual power plant load forecasting and dynamic regulation optimization system according to claim 1, characterized in that, Also includes: The device fingerprint database construction module transmits the dynamic response time delay coefficient in the generated device fingerprint code to the dynamic clustering module, and transmits the adjustment margin quantification value in the device fingerprint code to the multi-time scale prediction module; The dynamic clustering module, based on the received dynamic response time delay coefficient, combined with the geographical coordinates of the energy equipment and the power regulation rate threshold, triggers the region division process in the spatial clustering algorithm; The multi-timescale prediction module adjusts the input weight allocation associated with regional topological constraints in the spatiotemporal graph convolutional network based on the received adjustment margin quantification.

3. The virtual power plant load forecasting and dynamic regulation optimization system according to claim 2, characterized in that, Also includes: The regional simulation sub-model generated by the dynamic clustering module includes regional topological constraint parameters and power regulation rate thresholds. The regional simulation sub-models are synchronously transmitted to the digital twin simulation module and the multi-energy flow regulation module; The digital twin simulation module divides the boundary conditions of the equipment group for electromagnetic transient simulation based on the regional topology constraint parameters in the regional simulation sub-model. The multi-energy flow regulation module extracts the power regulation rate threshold from the regional simulation sub-model as a dynamic constraint parameter for constructing the energy efficiency conversion model of the electro-hydrogen conversion device and the thermal storage tank.

4. The virtual power plant load forecasting and dynamic regulation optimization system according to claim 3, characterized in that, Also includes: The multi-time-scale source load prediction data generated by the multi-time-scale prediction module includes power ramp features transformed from the power ramp rate parameters collected by the data acquisition module. Multi-time granularity source load prediction data are pushed in parallel to the digital twin simulation module and the cross-domain collaboration module; The digital twin simulation module generates power curtailment risk prediction results based on the power ramp-up characteristics in the received source-load prediction data and the equipment group boundary conditions of the regional simulation sub-model. The cross-domain collaboration module extracts timestamp information from the source load prediction data and performs time-series alignment verification with the energy equipment transaction records stored in the dual-chain blockchain architecture.

5. The virtual power plant load forecasting and dynamic regulation optimization system according to claim 4, characterized in that, Also includes: The dynamic response strategy generated by the digital twin simulation module includes energy storage charging and discharging thresholds and demand response priority parameters. The dynamic response strategy sends energy storage charging and discharging threshold update instructions to the multi-energy flow regulation module through the wide-area control module, and transmits the demand response priority parameters to the federated learning feature parameter pool of the cross-domain collaborative module. Based on the predicted results of power curtailment risk, the wide-area control module generates update instructions that match the energy storage charging and discharging thresholds; The cross-domain collaboration module triggers a pre-defined smart contract verification process in the blockchain architecture based on the received demand response priority parameters to authorize access to the federated learning feature parameter pool.

6. The virtual power plant load forecasting and dynamic regulation optimization system according to claim 5, characterized in that, Also includes: The scheduling instruction sequence output by the wide-area control module includes communication interruption compensation instructions generated based on energy storage charging and discharging thresholds, as well as edge node reinforcement learning model parameters extracted after authorization from the federated learning feature parameter pool; The cross-domain collaboration module decomposes the scheduling instruction sequence into communication interruption compensation instructions and reinforcement learning model parameters. The communication interruption compensation instructions are pushed to the multi-energy flow regulation module to reversely correct the dynamic constraint parameters in the thermal storage tank energy efficiency conversion model. The parameters of the reinforcement learning model are written through the sidechain storage nodes of the dual-chain blockchain architecture and bound to the transaction records after time-series alignment verification for data integrity.

7. The virtual power plant load forecasting and dynamic regulation optimization system according to claim 6, characterized in that, Also includes: Based on the authorized access permissions of the federated learning feature parameter pool, the cross-domain collaboration module completes zero-knowledge proof verification and then extracts the hydrogen energy storage virtual capacity mapping parameters from the sidechain storage node bound to data integrity. The cross-domain collaboration module sends the verified virtual capacity mapping parameters of hydrogen energy storage back to the multi-energy flow regulation module, and, in conjunction with the dynamic constraint parameters of the energy efficiency conversion model, triggers the capacity redistribution calculation between the electro-hydrogen conversion device and the thermal storage tank. Based on the capacity redistribution results, the multi-energy flow regulation module generates load transfer path planning parameters in the multi-energy flow collaborative control strategy that match the power curtailment risk prediction results.

8. The virtual power plant load forecasting and dynamic regulation optimization system according to claim 7, characterized in that, Also includes: The cross-domain collaboration module is equipped with a data trust verification unit, which receives the encrypted identification signal from the federated learning feature parameter pool; The data trust verification unit connects to the distributed ledger node of the blockchain architecture and retrieves the integrity verification code of the hydrogen energy storage virtual capacity mapping parameters. The data credibility verification unit outputs the verified hydrogen energy storage virtual capacity mapping parameters to the capacity dynamic allocation unit of the multi-energy flow regulation module. The encrypted identifier signal of the federated learning feature parameter pool triggers the data trust verification unit. The data trust verification unit completes the verification of hydrogen energy storage parameters through the blockchain distributed ledger, and the verification result is transmitted to the capacity dynamic allocation unit.

9. The virtual power plant load forecasting and dynamic regulation optimization system according to claim 8, characterized in that, Also includes: The capacity dynamic allocation unit collects real-time data on the power fluctuation of the electro-hydrogen conversion unit and the thermal inertia energy buffer index of the thermal storage tank. The capacity dynamic allocation unit analyzes the schedulable capacity threshold in the hydrogen energy storage virtual capacity mapping parameters and associates it with the energy conversion efficiency parameters of the electro-hydrogen conversion device and the thermal storage tank. The capacity dynamic allocation unit generates an electro-hydrogen conversion priority instruction and sends it to the load transfer path planning unit. The power data of the electro-hydrogen conversion unit and the buffer index of the thermal storage tank are input into the capacity dynamic allocation unit. The capacity dynamic allocation unit outputs the electro-hydrogen conversion priority instruction to the path planning unit based on the scheduling threshold and conversion efficiency parameters in the hydrogen energy storage mapping parameters.

10. The virtual power plant load forecasting and dynamic regulation optimization system according to claim 9, characterized in that, Also includes: The load transfer path planning unit receives the power curtailment risk quantification signal from the digital twin simulation module; The load transfer path planning unit compares the conflicting nodes between the priority command for the electro-hydrogen conversion and the power curtailment risk quantification signal, and reconstructs the thermal energy transmission path of the thermal storage tank and the electrolysis power allocation ratio of the electro-hydrogen conversion unit. The load transfer path planning unit feeds back a load transfer topology adapted to the current power curtailment risk level to the execution terminal; The power curtailment risk quantification signal triggers the path planning unit, which reconstructs the thermal storage tank transportation path and electrolysis power allocation through conflict node analysis, and generates risk-adaptive topology instructions to the execution terminal.

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